DSPRiverInspection/code_bak/master_0509.py

import numpy as np
import time,ast,copy
from flask import request, Flask,jsonify
import base64,cv2,os,sys,json
sys.path.extend(['../yolov5'])  
#from Send_tranfer import b64encode_function,JsonSend,name_dic,nameID_dic,getLogFileFp 
from segutils.segmodel import SegModel,get_largest_contours
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.torch_utils import select_device, load_classifier, time_synchronized
from queRiver import get_labelnames,get_label_arrays,post_process_,save_problem_images,time_str
import subprocess as sp
import matplotlib.pyplot as plt
import torch,random,string
import multiprocessing
from multiprocessing import Process,Queue
import traceback
from kafka import KafkaProducer, KafkaConsumer,TopicPartition
from kafka.errors import kafka_errors

#torch.multiprocessing.set_start_method('spawn')
import utilsK
from utilsK.GPUtils import *
from utilsK.masterUtils import *
from utilsK.sendUtils import create_status_msg,update_json

#from utilsK.modelEval import onlineModelProcess
import random,string
from Send_tranfer_oss import msg_dict_on,msg_dict_off
import pykafka 
from pykafka import KafkaClient 
process_id=0

def onlineModelProcess(parIn  ):
    DEBUG=False
    streamName = parIn['streamName']
    childCallback=parIn['callback']
    outStrList={}
    #try:
    for wan in ['test']:
        jsonfile=parIn['modelJson']
        with open(jsonfile,'r') as fp:
            parAll = json.load(fp)
            
        Detweights=parAll['gpu_process']['det_weights']
        seg_nclass = parAll['gpu_process']['seg_nclass']
        Segweights = parAll['gpu_process']['seg_weights']
        videoSave =  parAll['AI_video_save']
        imageTxtFile =  parAll['imageTxtFile']
        taskId,msgId = streamName.split('-')[1:3]
  
        inSource,outSource=parIn['inSource'],parIn['outSource']
        
        ##构建日志文件
        if outSource != 'NO':
            logdir = parAll['logChildProcessOnline'] 
            waitingTime=parAll['StreamWaitingTime']   
        else:        
            logdir = parAll['logChildProcessOffline']    
            waitingTime=5  
          
        fp_log=create_logFile(logdir=logdir) 
        kafka_par=parIn['kafka_par']
        producer = KafkaProducer(bootstrap_servers=kafka_par['server'],value_serializer=lambda v: v.encode('utf-8'),metadata_max_age_ms=120000)

        ####要先检查视频的有效性
        ###开始的时候,如果在线任务没有流，要发送的心跳消息，msg_h,
        msg_h=   copy.deepcopy(msg_dict_off);
        msg_h['status']='waiting';msg_h['msg_id']=msgId
       

        if outSource == 'NO': 
            msg_h['type']=1
            Stream_ok= get_fps_rtmp(inSource,video=True) 
        else:
            msg_h['type']=2         
            
            msg_h_d = json.dumps(msg_h, ensure_ascii=False) 
            outStrList['success']= '%s waiting stream or video, send heartbeat:  taskId:%s  msgId:%s  send:%s'%('-'*20,taskId, msgId,msg_h);
            outStrList['failure']='#######kafka ERROR waiting stream or video, send heartbeat'
            outStrList['Refailure']='##############kafka ERROR waiting stream or video, Re-send heartbeat'                
            Stream_ok=check_stream(inSource,producer,kafka_par,msg_h_d,outStrList,fp_log ,timeMs=waitingTime)
        
        if  Stream_ok:###发送开始信号
            msg_h['status']='running'
            msg_h_d = json.dumps(msg_h, ensure_ascii=False) 
            
            outStrList['success']= '%s informing stream/video is ok, taskId:%s  msgId:%s  send:%s'%('-'*20,taskId, msgId,msg_h);
            outStrList['failure']='#######kafka ERROR ,when informing stream/video is ok'
            outStrList['Refailure']='##############kafka ERROR, when re-informing stream/video is ok'    
        
            send_kafka(producer,kafka_par,msg_h_d,outStrList,fp_log     );
            
        else:
            ####检测离线视频是否有效,无效要报错
            outstr='############# offline vedio or live stream  Error:%s  #################'%(inSource)
            outstr=wrtiteLog(fp_log,outstr);print( outstr);
            
            msg_h['error']=str(1001);msg_h['status']='failed';
            msg_h_d = json.dumps(msg_h, ensure_ascii=False);
            outStrList['success']= '%s informing invaid video or stream success :  taskId:%s  msgId:%s  send:%s'%('-'*20,taskId, msgId,msg_h);
            outStrList['failure']='#######kafka ERROR, when  informing invaid video or stream'
            outStrList['Refailure']='##############kafka ERROR,when re-informing invaid video or stream'    
            send_kafka(producer,kafka_par,msg_h_d,outStrList,fp_log     ); 
            childCallback.send(' offline vedio or live stream  Error')  
            continue
            
        
       
        if (inSource.endswith('.MP4')) or (inSource.endswith('.mp4')):
            fps,outW,outH,totalcnt=get_fps_rtmp(inSource,video=True)[0:4]
        else:
            fps,outW,outH,totalcnt=get_fps_rtmp(inSource,video=False)[0:4]
        fps = int(fps+0.5)       
        if outSource != 'NO':
            command=['ffmpeg','-y','-f', 'rawvideo','-vcodec','rawvideo','-pix_fmt', 'bgr24',
                '-s', "{}x{}".format(outW,outH),# 图片分辨率           
                '-r', str(fps),# 视频帧率
                '-i', '-','-c:v', 'libx264','-pix_fmt', 'yuv420p',
                '-f', 'flv',outSource
                ]
            video_flag = videoSave['onLine']      
            logdir = parAll['logChildProcessOnline'] 
            waitingTime=parAll['StreamWaitingTime']   
        else:        
            video_flag = videoSave['offLine'] ;logdir = parAll['logChildProcessOffline']    
            waitingTime=5  
                   
        fp_log=create_logFile(logdir=logdir) 
        

        device = select_device(parIn['device'])
        half = device.type != 'cpu'  # half precision only supported on CUDA
        model = attempt_load(Detweights, map_location=device)  # load FP32 model
        if half: model.half()
  

        segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)

           
        
        # 管道配置，其中用到管道
        if outSource !='NO' :
            ppipe = sp.Popen(command, stdin=sp.PIPE)
           
    
        ##后处理参数
        par=parAll['post_process']
        conf_thres,iou_thres,classes=par['conf_thres'],par['iou_thres'],par['classes']
        outImaDir = par['outImaDir']
        outVideoDir = par['outVideoDir']
        labelnames=par['labelnames']
        rainbows=par['rainbows']
        fpsample = par['fpsample']
        names=get_labelnames(labelnames)
        label_arraylist = get_label_arrays(names,rainbows,outfontsize=40)
        
        dataset = LoadStreams(inSource, img_size=640, stride=32)
        
    
        childCallback.send('####model load success####')    
        if (outVideoDir!='NO')  and video_flag:
            msg_id = streamName.split('-')[2]
            save_path = os.path.join(outVideoDir,msg_id+'.MP4')
            vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (outW,outH))
  
        iframe = 0;post_results=[];time_beg=time.time()
        
        t00=time.time()
        time_kafka0=time.time()
        for path, img, im0s, vid_cap in dataset:
          
            t0= time_synchronized()
            if  not path:
                EndUrl='%s/%s_frame-9999-9999_type-结束_9999999999999999_s-%s_AI.jpg'%(outImaDir,time_str(),streamName)
                EndUrl = EndUrl.replace(' ','-').replace(':','-')
                img_end=np.zeros((100,100),dtype=np.uint8);cv2.imwrite(EndUrl,img_end)
                if imageTxtFile:
                    EndUrl_txt = EndUrl.replace('.jpg','.txt')
                    fp_t=open(EndUrl_txt,'w');fp_t.write(EndUrl+'\n');fp_t.close()
                    
                EndUrl='%s/%s_frame-9999-9999_type-结束_9999999999999999_s-%s_OR.jpg'%(outImaDir,time_str(),streamName)
                EndUrl = EndUrl.replace(' ','-').replace(':','-')
                ret = cv2.imwrite(EndUrl,img_end)
                if imageTxtFile:
                    EndUrl_txt = EndUrl.replace('.jpg','.txt')
                    fp_t=open(EndUrl_txt,'w');fp_t.write(EndUrl+'\n');fp_t.close()
                
                #print(EndUrl,ret)
                childCallback.send('####strem ends####')
                if (outVideoDir!='NO')  and video_flag:
                    vid_writer.release()  
                break###断流或者到终点
   
            if outSource == 'NO':###如果不推流，则显示进度条
                view_bar(iframe,totalcnt,time_beg ,parIn['process_uid'] )
             
            ###直播和离线都是1分钟发一次消息。直播发 
            time_kafka1 = time.time()
            if time_kafka1 - time_kafka0 >60:
                time_kafka0 = time_kafka1
                ###发送状态信息waiting
                msg = copy.deepcopy(msg_dict_off);
                msg['msg_id']= msgId; msg
                if outSource == 'NO':
                    msg['progressbar']= '%.4f'%(iframe*1.0/totalcnt)
                    msg['type']=1
                else:
                    msg['progressbarOn']= str(iframe)
                    msg['type']=2
                
                msg = json.dumps(msg, ensure_ascii=False)    
                '''
                try:
                    record_metadata = producer.send(kafka_par['topic'], msg).get()
                    outstr='%s processing send progressbar or heartBeat to kafka:  taskId:%s  msgId:%s  send:%s'%('-'*20,taskId, msgId,msg);
                    wrtiteLog(fp_log,outstr);print( outstr);
                except Exception as e:
                    outstr='#######kafka ERROR when  processing sending progressbar or heartBeat:, error: %s'%(str(e))
                    wrtiteLog(fp_log,outstr);print( outstr);
                    try:
                        producer = KafkaProducer(bootstrap_servers=par['server'], value_serializer=lambda v: v.encode('utf-8')).get()
                        future = producer.send(par['topic'][2], msg).get()
                    except Exception as e:
                        outstr='%s re-send progressbar or  heartBeat kafka,processing video or stream:  taskId:%s  msgId:%s  send:%s'%('-'*20,taskId, msgId,msg);
                        wrtiteLog(fp_log,outstr);print( outstr);
                '''         
                        
                        
                ###发送状态信息waiting
                outStrList['success']= '%s processing send progressbar or heartBeat to kafka:  taskId:%s  msgId:%s  send:%s'%('-'*20,taskId, msgId,msg);
                outStrList['failure']='#######kafka ERROR  when  processing sending progressbar or heartBeat'
                outStrList['Refailure']='%s re-send progressbar or  heartBeat kafka,processing video or stream:  taskId:%s  msgId:%s  send:%s'%('-'*20,taskId, msgId,msg);
                send_kafka(producer,kafka_par,msg,outStrList,fp_log     ); 
                
             
    
            time0=time.time()
            iframe +=1
            time1=time.time()
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()  # uint8 to fp16/32
  
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            
            timeseg0 = time.time()
            seg_pred,segstr = segmodel.eval(im0s[0] )
            timeseg1 = time.time()
            
            t1= time_synchronized()
            pred = model(img,augment=False)[0]
            time4 = time.time()
            datas = [path, img, im0s, vid_cap,pred,seg_pred,iframe]
            
            p_result,timeOut = post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,iframe)
            t2= time_synchronized()
            
            #print('###line138:',timeOut,outSource,outVideoDir)
            ##每隔 fpsample帧处理一次，如果有问题就保存图片       
            if (iframe % fpsample == 0) and (len(post_results)>0) :
                parImage=save_problem_images(post_results,iframe,names,streamName=streamName,outImaDir='problems/images_tmp',imageTxtFile=imageTxtFile)
                post_results=[]   

            if len(p_result[2] )>0:  ##                
                post_results.append(p_result)
            t3= time_synchronized()
            image_array = p_result[1]
            if outSource!='NO':
                ppipe.stdin.write(image_array.tobytes())  
  
            if (outVideoDir!='NO')  and video_flag:    
                ret = vid_writer.write(image_array)    
            t4= time_synchronized()
            
            timestr2 = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) 
            if iframe%100==0:
                outstr='%s,,read:%.1f ms,copy:%.1f, infer:%.1f ms, detinfer:%.1f ms,draw:%.1f ms,  save:%.1f ms   total:%.1f ms \n'%(timestr2,(t0 - t00)*1000,(timeseg0-t0)*1000, (t1 - timeseg0)*1000,(t2-t1)*1000, (t3 - t2)*1000,(t4-t3)*1000, (t4-t00)*1000)
                wrtiteLog(fp_log,outstr);
            #print(outstr)
            t00 = t4;  
            
               
                        
                        
            
    ##模型加载之类的错误  
    #except  Exception as e:
  
    #    print(time.strftime("%Y-%m-%d %H:%M:%S  ", time.localtime()) ,'*'*20,'###line177 ERROR:',e)
    #    childCallback.send(e)  #将异常通过管道送出       
        
                   
def lauch_process(gpuid,inSource,outSource,taskId,msgId,modelJson,kafka_par):

    if outSource=='NO':
        streamName='off-%s-%s'%(taskId,msgId)
    else:
        streamName='live-%s-%s'%(taskId,msgId)    
    dataPar ={
            'imgData':'',
            'imgName':'testW',
             'streamName':streamName,
             'taskId':taskId,
             'msgId':msgId,
             'device':str(gpuid),
             'modelJson':modelJson,
             'kafka_par':kafka_par,
             }   
    #dataPar['inSource'] = 'http://images.5gai.taauav.com/video/8bc32984dd893930dabb2856eb92b4d1.mp4';dataPar['outSource'] = None
    dataPar['inSource'] = inSource;dataPar['outSource'] = outSource    
    process_uid=''.join(random.sample(string.ascii_letters + string.digits, 16));dataPar['process_uid']=process_uid
    parent_conn, child_conn = multiprocessing.Pipe();dataPar['callback']=child_conn
    gpuProcess=Process(target=onlineModelProcess,name='process:%s'%( process_uid ),args=(dataPar,))
    gpuProcess.start()
    #print(dir(gpuProcess))
    #child_return = parent_conn.recv()
    #timestr2=time.strftime("%Y-%m-%d %H:%M:%S  ", time.localtime())
    #print(timestr2,'-'*20,'progress:%s ,msgId:%s , taskId:%s  return:'%(process_uid,msgId,taskId),child_return)
    
    return gpuProcess

         
msg_dict_offline = {
                    
                    "biz_id":"hehuzhang",
                    "mod_id":"ai",
                    "msg_id":'bb'+''.join(random.sample(string.ascii_letters ,30)  ) ,
                    "offering_id":"http://vod.play.t-aaron.com/customerTrans/c49a2c620795d124f2ae4b10197b8d0e/303b7a58-17f3ef4494e-0004-f90c-f2c-7ec68.mp4",
                    "offering_type":"mp4",
                    "results_base_dir": "XJRW202203171535"+str(random.randint(10,99)),
                    
                    'outSource':'NO'
                    
                    
                    }



def detector(par):
    ####初始化信息列表
    consumer = KafkaConsumer(bootstrap_servers=par['server'],client_id='AI_server',group_id=par['group_id'],auto_offset_reset='earliest')
                    
                    
    consumer.subscribe( par['topic'][0:2]) 
    '''
    client = KafkaClient(hosts=par['server'])
    consumer_pys=[]
    for topic_name in par['topic'][0:2]:
        consumer_pys.append(client.topics[  topic_name ].get_simple_consumer(consumer_group=par['group_id'],timeout=30))
   
    '''
    
    kafka_par ={ 'server':par['server'],'topic':par['topic'][2]  }
    producer = KafkaProducer(
                    bootstrap_servers=par['server'],#tencent yun 
                    value_serializer=lambda v: v.encode('utf-8'),
                    metadata_max_age_ms=120000)
                    
    taskStatus={} 
    taskStatus['onLine'] = Queue(100)
    taskStatus['offLine']= Queue(100)   
    taskStatus['pidInfos']= {}
    
    fp_log=create_logFile(logdir=par['logDir'])
    wrtiteLog(fp_log,'###########masster starts in line222######\n')
    
    timeSleep=1
    
    #taskStatus['pidInfos'][31897]={'gpuProcess':'onlineProcess','type':'onLine'}
    
        
    time0=time.time()
    time0_kafQuery=time.time()
    time0_taskQuery=time.time()
    time0_sleep=time.time()
    time_interval=10;  outStrList={}
    while True:###每隔timeSleep秒，轮询一次
    #for isleep in range(1):
        '''
        ##1-读取kafka，更新任务类别
        try:
           
            msgs = getAllRecords(consumer,par['topic'])
        except Exception as e:
            outstr='%s   kafka connecting error:%s '%('#'*20,e)
            outstr=wrtiteLog(fp_log,outstr);print( outstr);
            time.sleep(timeSleep)
            continue
        #if get_whether_gpuProcess():
        
        time0_kafQuery,printFlag = check_time_interval(time0_kafQuery,time_interval)
        if printFlag:
            outstr_kafka=' ##### kafka Left %d records####'%(len(msgs));
            outstr_kafka=wrtiteLog(fp_log,outstr_kafka)
            
        '''    
            
        for ii,msg in enumerate(consumer):
        #for  ii,msg in enumerate(msgs):
            ##读取消息
            try:
             
                taskInfos = eval(msg.value.decode('utf-8')  )  
              
            except:
                   
                outstr='%s   msg format error,value:%s,offset:%d partition:%s topic:%s'%('#'*20,msg.value,msg.offset,msg.topic,msg.topic)
                continue
                    
            if msg.topic == par['topic'][0]: ##
                                 
                taskInfos['inSource']= taskInfos['pull_channel']; 
                taskInfos['outSource']=  get_push_address(taskInfos['push_channel']) ;    
                         
                taskStatus['onLine'].put( taskInfos )   
                save_message(par['kafka'],taskInfos)     
                
                ###发送状态信息waiting
                msg = create_status_msg(msg_dict_on,taskInfos,sts='waiting')       
                outStrList['success']= '%s read from kafka online task and back to kafka:  taskId:%s  msgId:%s  send:%s'%('-'*20,taskInfos['results_base_dir'], taskInfos['msg_id'],msg)
                outStrList['failure']='#######kafka ERROR when read from kafka online task and back to kafka'
                outStrList['Refailure']='##############kafka ERROR when read from kafka online task and resend back to kafka:'
                send_kafka(producer,kafka_par,msg,outStrList,fp_log     ); 
            
            else:    
               
                taskInfos['inSource']= taskInfos['offering_id']; 
                taskInfos['outSource']= 'NO'
               
                taskStatus['offLine'].put( taskInfos )
                save_message(par['kafka'],taskInfos)   

                ###发送状态信息waiting 
                msg = create_status_msg(msg_dict_off,taskInfos,sts='waiting')   


                outStrList['success']= '%s read from kafka offline task and back to kafka:  taskId:%s  msgId:%s  send:%s'%('-'*20,taskInfos['results_base_dir'], taskInfos['msg_id'],msg)
                outStrList['failure']='#######kkafka ERROR when read from kafka offline task and back to kafka:,'
                outStrList['Refailure']='##############kafka ERROR when read from kafka offline task and resend back to kafka:'
                send_kafka(producer,kafka_par,msg,outStrList,fp_log     ); 
                
               

            time0_taskQuery,printFlag = check_time_interval(time0_taskQuery,time_interval)
            outstr_task=  ' task queue onLine cnt:%d    offLine:%d'%(taskStatus['onLine'].qsize(), taskStatus['offLine'].qsize())      
                
            ##2-更新显卡信息
            gpuStatus = getGPUInfos()
            
            ##3-优先考虑在线任务
            if not taskStatus['onLine'].empty():
                ###3.1-先判断有没有空闲显卡：
                cuda = get_available_gpu(gpuStatus)
                 ###获取在线任务信息，并执行,lauch process
                taskInfos =   taskStatus['onLine'].get() 
                
                print('################396',cuda)    
                if cuda: ###3.1.1 -有空余显卡
                    #lauch process
           
                    msg=   copy.deepcopy(msg_dict_on);

                    gpuProcess=lauch_process(cuda,taskInfos['inSource'],taskInfos['outSource'],taskInfos['results_base_dir'],taskInfos['msg_id'],par['modelJson'],kafka_par)
     
                    taskStatus['pidInfos'][gpuProcess.pid] = {'gpuProcess':gpuProcess,'type':'onLine','taskInfos':taskInfos}
                    
                    
                else:###3.1.2-没有显卡
                    ##判断有没有显卡上面都是离线进程的
                    cuda_pid = get_potential_gpu(gpuStatus,taskStatus['pidInfos'])
              
                    if cuda_pid:#3.1.2.1 - ##如果有可以杀死的进程
                        cuda = cuda_pid['cuda']
                        pids = cuda_pid['pids']
                        ##kill 离线进程，并更新离线任务表
                        cnt_off_0 = taskStatus['offLine'].qsize()
                        for pid in pids:
                            ##kill 离线进程
                            taskStatus['pidInfos'][pid]['gpuProcess'].kill()
                            ##更新离线任务表
                            taskStatus['offLine'].put( taskStatus['pidInfos'][pid]['taskInfos']    )
                            taskInfos_off=taskStatus['pidInfos'][pid]['taskInfos'] 
                            
                            ##发送离线数据，说明状态变成waiting
                            msg=   msg_dict_off;
                            msg=update_json(taskInfos_off,msg,offkeys=["msg_id","biz_id" ,"mod_id"] )
                            msg['results'][0]['original_url']=taskInfos_off['inSource']
                            msg['results'][0]['sign_url']=get_boradcast_address(taskInfos_off['outSource']) 
                            msg['status']='waiting'                        
                            msg = json.dumps(msg, ensure_ascii=False)  

                            outStrList['success']= '%s start online task after kill offline tasks and back to kafka: pid:%d  taskId:%s  msgId:%s  send:%s'%('-'*20,gpuProcess.pid,taskInfos_off['results_base_dir'], taskInfos_off['msg_id'],msg) 
                            outStrList['failure']='#######kafka ERROR when start online task after kill offline tasks and back to kafka'
                            outStrList['Refailure']='##############kkafka ERROR when start online task after kill offline tasks and back to kafka'
                            send_kafka(producer,kafka_par,msg,outStrList,fp_log     ); 
                            
                            
                        cnt_off_1 = taskStatus['offLine'].qsize()
                        outstr='%s before killing process, offtask cnt:%d  ,after killing, offtask cnt:%d %s'%('-'*20 ,cnt_off_0,cnt_off_1,'*'*20)
                        outstr=wrtiteLog(fp_log,outstr);print( outstr); 
                        gpuProcess=lauch_process(cuda,taskInfos['inSource'],taskInfos['outSource'],taskInfos['results_base_dir'],taskInfos['msg_id'],par['modelJson'],kafka_par)
                    
                                                
                        ###更新pidinfos，update pidInfos
                        taskStatus['pidInfos'][gpuProcess.pid] = {'gpuProcess':gpuProcess,'type':'onLine','taskInfos':taskInfos}
                        
                       

                    else:
                        outstr='######No available GPUs for onLine####'
                        outstr=wrtiteLog(fp_log,outstr);print( outstr);        
            ##4-更新显卡信息
            gpuStatus = getGPUInfos()        
            ##5-考虑离线任务
            if not taskStatus['offLine'].empty():
                cudaArrange= arrange_offlineProcess(gpuStatus,taskStatus['pidInfos'],modelMemory=1500)
                outstr='###line342 IN OFF LINE TASKS available cudas:%s'%(cudaArrange)
                outstr=wrtiteLog(fp_log,outstr);print( outstr);      
                for cuda in cudaArrange:
                    if  not taskStatus['offLine'].empty():
                        taskInfos =   taskStatus['offLine'].get()
                        
                       
                    
                        gpuProcess=lauch_process(cuda,taskInfos['inSource'],taskInfos['outSource'],taskInfos['results_base_dir'],taskInfos['msg_id'],par['modelJson'],kafka_par)
                        taskStatus['pidInfos'][gpuProcess.pid] = {'gpuProcess':gpuProcess,'type':'offLine','taskInfos':taskInfos}   
                        
                      
                       
                        
            if get_whether_gpuProcess():                 
                time0_sleep,printFlag = check_time_interval(time0_sleep,time_interval)
                if printFlag:
                   timestr2=time.strftime("%Y-%m-%d %H:%M:%S  ", time.localtime()) 
                   outstr= timestr2 + '*'*20 +'sleep '+'*'*20;  
                   outstr=wrtiteLog(fp_log,outstr);print( outstr);
                   outstr_task=wrtiteLog(fp_log,outstr_task);print( outstr_task);
               
        time.sleep(timeSleep)
        print('########sleep 1s #####')
    print('########Program End#####')
    

                    
if __name__ == '__main__':
    par={};
    ###topic0--在线，topic1--离线

    #par['server']='212.129.223.66:9092';par['topic']=('thsw','thsw2','testReturn');par['group_id']='test';
    #101.132.127.1:19092  
    
    '''
    par['server']='101.132.127.1:19092 ';par['topic']=('alg-online-tasks','alg-offline-tasks','alg-task-results');par['group_id']='test';
    
    par['kafka']='mintors/kafka'
    par['modelJson']='conf/model.json'
    '''
    masterFile="conf/master.json"
    assert os.path.exists(masterFile)
    with open(masterFile,'r') as fp:
        data=json.load(fp)   
    par=data['par']        
    print(par)   
    detector(par)