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 onlineModelProcsss import random,string from Send_tranfer_oss import msg_dict_on,msg_dict_off process_id=0 def onlineModelProcess(parIn ): DEBUG=False streamName = parIn['streamName'] childCallback=parIn['callback'] outStrList={} channelIndex=parIn['channelIndex'] #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'] StreamRecoveringTime=int(parAll['StreamRecoveringTime']) 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 logname='gpuprocess.log' fp_log=create_logFile(logdir=logdir,name=logname) logger=logdir.replace('/','.')+'.'+logname 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 thread='master:gpuprocess-%s'%(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=get_infos(taskId, msgId,msg_h_d,key_str='waiting stream or video, send heartbeat') Stream_ok=check_stream(inSource,producer,kafka_par,msg_h_d,outStrList,fp_log,logger,line=sys._getframe().f_lineno,thread=thread ,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= get_infos(taskId, msgId,msg_h_d,key_str='informing stream/video is ok') send_kafka(producer,kafka_par,msg_h_d,outStrList,fp_log,line=sys._getframe().f_lineno,logger=logger,thread=thread ); 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); writeELK_log(msg=outstr,fp=fp_log,level='ERROR',line=sys._getframe().f_lineno,logger=logger) msg_h['error']='Stream or video ERROR';msg_h['status']='failed'; msg_h_d = json.dumps(msg_h, ensure_ascii=False); outStrList= get_infos(taskId, msgId,msg_h_d,key_str='informing invaid video or stream success') send_kafka(producer,kafka_par,msg_h_d,outStrList,fp_log ,line=sys._getframe().f_lineno,logger=logger,thread=thread ); 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)[1][0:4]; else: fps,outW,outH,totalcnt=get_fps_rtmp(inSource,video=False)[1][0:4] fps = int(fps+0.5) if fps>30: fps=25 ###线下测试时候，有时候读帧率是9000，明显不符合实际，所以加这个判断。 if outSource != 'NO': command=['/usr/bin/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 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) ##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####') print('#####line153:',outVideoDir,video_flag) if (outVideoDir!='NO') : ####2022.06.27新增在线任务也要传AI视频和原始视频 if 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)) if vid_writer.isOpened(): outstr='touch video success:%s'%(save_path);level='INFO' else:outstr='touch video failed:%s'%(save_path);level='ERROR' writeELK_log(msg=outstr,fp=fp_log,level=level,line=sys._getframe().f_lineno,logger=logger) else: msg_id = streamName.split('-')[2] save_path_OR = os.path.join(outVideoDir,msg_id+'_OR.MP4') vid_writer_OR = cv2.VideoWriter(save_path_OR, cv2.VideoWriter_fourcc(*'mp4v'), fps, (outW,outH)) save_path_AI = os.path.join(outVideoDir,msg_id+'_AI.MP4') vid_writer_AI = cv2.VideoWriter(save_path_AI, cv2.VideoWriter_fourcc(*'mp4v'), fps, (outW,outH)) if vid_writer_AI.isOpened() and vid_writer_OR.isOpened() :outstr='touch video success:%s,%s'%(save_path_OR,save_path_AI);level='INFO' else:outstr='touch video failed:%s,%s, fps:%d ,%d , %d'%(save_path_OR,save_path_AI,fps,outW,outH);level='ERROR' writeELK_log(msg=outstr,fp=fp_log,level=level,line=sys._getframe().f_lineno,logger=logger) iframe = 0;post_results=[];time_beg=time.time() t00=time.time() time_kafka0=time.time() Pushed_Flag=False while True: try: dataset = LoadStreams(inSource, img_size=640, stride=32) # 管道配置，其中用到管道 if outSource !='NO' and (not Pushed_Flag): ppipe = sp.Popen(command, stdin=sp.PIPE);Pushed_Flag = True for path, img, im0s, vid_cap in dataset: t0= time_synchronized() if outSource == 'NO':###如果不推流，则显示进度条。离线不推流 view_bar(iframe,totalcnt,time_beg ,parIn['process_uid'] ) streamCheckCnt=0 ###直播和离线都是1分钟发一次消息 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; 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) outStrList= get_infos(taskId, msgId,msg,key_str='processing send progressbar or online heartbeat') send_kafka(producer,kafka_par,msg,outStrList,fp_log,line=sys._getframe().f_lineno,logger=logger,thread=thread ); 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'): if video_flag: ret = vid_writer.write(image_array) else: time_w0=time.time() ret = vid_writer_AI.write(image_array) ret = vid_writer_OR.write(im0s[0]) time_w1=time.time() #if not ret: # print('\n write two videos time:%f ms'%(time_w1-time_w0)*1000,ret) 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); writeELK_log(msg=outstr,fp=fp_log,line=sys._getframe().f_lineno,logger=logger,printFlag=False) #print(outstr) t00 = t4; except Exception as e: #if outSource:###推流才有如下 streamCheckCnt+=1;taskEnd=False if streamCheckCnt==1:timeBreak0=time.time();time_kafka0 = time.time() timeBreak1=time.time(); if timeBreak1-timeBreak0 >5 and Pushed_Flag:###流断开5秒后，要关闭推流 ppipe.kill();Pushed_Flag=False writeELK_log(msg='stream pip is killed ',fp=fp_log,line=sys._getframe().f_lineno,logger=logger) ###读接口，看看任务有没有结束 ChanellInfos,taskEnd=query_channel_status(channelIndex) ####taskEnd######################DEBUG #taskEnd=False if timeBreak1-timeBreak0 >StreamRecoveringTime : ##默认30分钟内，流没有恢复的话，就断开。 taskEnd=True outstr_channel='%s ,taskEnd:%s'%(ChanellInfos,taskEnd) writeELK_log(msg=outstr_channel,fp=fp_log,line=sys._getframe().f_lineno,logger=logger) if outSource == 'NO':#离线没有推流 taskEnd=True if taskEnd: if timeBreak1-timeBreak0 > 60:###超时结束 writeTxtEndFlag(outImaDir,streamName,imageTxtFile,endFlag='超时结束') else: writeTxtEndFlag(outImaDir,streamName,imageTxtFile,endFlag='结束') if (outVideoDir!='NO'): if video_flag:vid_writer.release() else: vid_writer_OR.release(); vid_writer_AI.release(); outstr='Task ends:%.1f , msgid:%s,taskID:%s '%(timeBreak1-timeBreak0,taskId,msgId) writeELK_log(msg=outstr,fp=fp_log,line=sys._getframe().f_lineno,logger=logger) break ##执行到这里的一定是在线任务，在等待流的过程中要发送waiting time_kafka1 = time.time() if time_kafka1-time_kafka0>60: msg_res = copy.deepcopy(msg_dict_off); msg_res['msg_id']= msgId; msg_res['type']=2 msg_res = json.dumps(msg_res, ensure_ascii=False) outStrList= get_infos(taskId, msgId,msg_res,key_str='Waiting stream restoring heartbeat') send_kafka(producer,kafka_par,msg_res,outStrList,fp_log,line=sys._getframe().f_lineno,logger=logger,thread=thread ); outstr='Waiting stream recovering:%.1f s'%(timeBreak1-timeBreak0) writeELK_log(msg=outstr,fp=fp_log,line=sys._getframe().f_lineno,logger=logger) writeELK_log(msg=outstr_channel,fp=fp_log,line=sys._getframe().f_lineno,logger=logger) time_kafka0 = time_kafka1 #break###断流或者到终点 time.sleep(5) print('Waiting stream for ',e) def lauch_process(gpuid,inSource,outSource,taskId,msgId,modelJson,kafka_par,channelIndex='LC001'): 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, 'channelIndex':channelIndex, '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' } taskStatus={} taskStatus['onLine'] = Queue(100) taskStatus['offLine']= Queue(100) taskStatus['pidInfos']= {} def get_msg_from_kafka(par): thread='master:readingKafka' outStrList={} fp_log = par['fp_log'] logger=par['logger'] consumer = KafkaConsumer(bootstrap_servers=par['server'],client_id='AI_server',group_id=par['group_id'],auto_offset_reset='latest') consumer.subscribe( par['topic'][0:2]) outstr='reading kafka process starts' writeELK_log(msg=outstr,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) 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) for ii,msg in enumerate(consumer): ##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=get_infos(taskInfos['results_base_dir'], taskInfos['msg_id'],msg,key_str='read msgs from kafka online task and response to kafka') send_kafka(producer,kafka_par,msg,outStrList,fp_log,line=sys._getframe().f_lineno,logger=logger,thread=thread); else: try: 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=get_infos(taskInfos['results_base_dir'], taskInfos['msg_id'],msg,key_str='read msgs from kafka offline task and response to kafka') send_kafka(producer,kafka_par,msg,outStrList,fp_log ,line=sys._getframe().f_lineno,logger=logger,thread=thread ); except Exception as e: print('######msg Error######',msg,e) def detector(par): ####初始化信息列表 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) time_interval=par['logPrintInterval'] logname='detector.log';thread='master:detector' fp_log=create_logFile(logdir=par['logDir'],name=logname) ##准备日志函数所需参数 logger=par['logDir'].replace('/','.')+'.'+logname #wrtiteLog(fp_log,'########### detector process starts ######\n'); outstr='detector process starts';sys._getframe().f_lineno writeELK_log(msg=outstr,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) ###开启kafka consumer 进程## parIn=copy.deepcopy(par);parIn['fp_log']=fp_log ;parIn['logger']=logger HeartProcess=Process(target=get_msg_from_kafka,name='process-consumer-kafka',args=(parIn,)) HeartProcess.start() timeSleep=1 time0=time.time() time0_kafQuery=time.time() time0_taskQuery=time.time() time0_sleep=time.time() outStrList={} while True:###每隔timeSleep秒，轮询一次 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()) if (taskStatus['onLine'].qsize()>0) or (taskStatus['offLine'].qsize()>0): #outstr_task=wrtiteLog(fp_log,outstr_task);print( outstr_task); writeELK_log(msg=outstr_task,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) ##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() outstr='start to process onLine taskId:%s msgId:%s'%( taskInfos['results_base_dir'],taskInfos['msg_id'] ) #outstr=wrtiteLog(fp_log,outstr);print( outstr); writeELK_log(msg=outstr,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) 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,taskInfos['channel_code']) 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=get_infos(taskInfos_off['results_base_dir'], taskInfos_off['msg_id'],msg,key_str='start online task after kill offline tasks') send_kafka(producer,kafka_par,msg,outStrList,fp_log ,line=sys._getframe().f_lineno,logger=logger,thread=thread ); cnt_off_1 = taskStatus['offLine'].qsize() outstr='before killing process, offtask cnt:%d ,after killing, offtask cnt:%d '%(cnt_off_0,cnt_off_1) #outstr=wrtiteLog(fp_log,outstr);print( outstr); writeELK_log(msg=outstr,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) gpuProcess=lauch_process(cuda,taskInfos['inSource'],taskInfos['outSource'],taskInfos['results_base_dir'],taskInfos['msg_id'],par['modelJson'],kafka_par,taskInfos['channel_code']) ###更新pidinfos，update pidInfos taskStatus['pidInfos'][gpuProcess.pid] = {'gpuProcess':gpuProcess,'type':'onLine','taskInfos':taskInfos} else: outstr='No available GPUs for onLine task' #outstr=wrtiteLog(fp_log,outstr);print(outstr); writeELK_log(msg=outstr,fp=fp_log,level='ERROR',thread=thread,line=sys._getframe().f_lineno,logger=logger) ##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='IN OFF LINE TASKS available cudas:%s'%(cudaArrange) #outstr=wrtiteLog(fp_log,outstr);print( outstr); writeELK_log(msg=outstr,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) for cuda in cudaArrange: if not taskStatus['offLine'].empty(): taskInfos = taskStatus['offLine'].get() outstr='start to process offLine taskId:%s msgId:%s'%( taskInfos['results_base_dir'],taskInfos['msg_id'] ) taskInfos['channel_code']='LC999'###离线消息没有这个参数 #outstr=wrtiteLog(fp_log,outstr);print( outstr); writeELK_log(msg=outstr,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) gpuProcess=lauch_process(cuda,taskInfos['inSource'],taskInfos['outSource'],taskInfos['results_base_dir'],taskInfos['msg_id'],par['modelJson'],kafka_par,taskInfos['channel_code']) 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: outstr= '*'*20 +'sleep '+'*'*20; #outstr=wrtiteLog(fp_log,outstr);print( outstr); writeELK_log(msg=outstr,fp=fp_log,thread=thread,line=sys._getframe().f_lineno,logger=logger) time.sleep(timeSleep) 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)