421 lines
16 KiB
Python
421 lines
16 KiB
Python
import numpy as np
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import time,ast,copy
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from flask import request, Flask,jsonify
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import base64,cv2,os,sys,json
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sys.path.extend(['../yolov5'])
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#from Send_tranfer import b64encode_function,JsonSend,name_dic,nameID_dic,getLogFileFp
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from segutils.segmodel import SegModel,get_largest_contours
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from models.experimental import attempt_load
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from utils.datasets import LoadStreams, LoadImages
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from utils.torch_utils import select_device, load_classifier, time_synchronized
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from queRiver import get_labelnames,get_label_arrays,post_process_,save_problem_images,time_str
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import subprocess as sp
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import matplotlib.pyplot as plt
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import torch,random,string
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import multiprocessing
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from multiprocessing import Process,Queue
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import traceback
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from kafka import KafkaProducer, KafkaConsumer,TopicPartition
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from kafka.errors import kafka_errors
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#torch.multiprocessing.set_start_method('spawn')
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import utilsK
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from utilsK.GPUtils import *
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from utilsK.masterUtils import *
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from utilsK.sendUtils import create_status_msg,update_json
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#from utilsK.modelEval import onlineModelProcess
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import random,string
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from Send_tranfer_oss import msg_dict_on,msg_dict_off
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process_id=0
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def onlineModelProcess(parIn ):
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DEBUG=False
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streamName = parIn['streamName']
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childCallback=parIn['callback']
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#try:
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for wan in ['test']:
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jsonfile=parIn['modelJson']
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with open(jsonfile,'r') as fp:
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parAll = json.load(fp)
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Detweights=parAll['gpu_process']['det_weights']
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seg_nclass = parAll['gpu_process']['seg_nclass']
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Segweights = parAll['gpu_process']['seg_weights']
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videoSave = parAll['AI_video_save']
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imageTxtFile = parAll['imageTxtFile']
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inSource,outSource=parIn['inSource'],parIn['outSource']
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kafka_par=parIn['kafka_par']
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producer = KafkaProducer(bootstrap_servers=kafka_par['server'],value_serializer=lambda v: v.encode('utf-8'),metadata_max_age_ms=120000)
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device = select_device(parIn['device'])
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half = device.type != 'cpu' # half precision only supported on CUDA
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model = attempt_load(Detweights, map_location=device) # load FP32 model
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if half: model.half()
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#print('###line116:,',len(dataset),dataset)
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if (inSource.endswith('.MP4')) or (inSource.endswith('.mp4')):
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fps,outW,outH,totalcnt=get_fps_rtmp(inSource,video=True)[0:4]
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else:
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fps,outW,outH,totalcnt=get_fps_rtmp(inSource,video=False)[0:4]
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fps = int(fps+0.5)
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segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
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if outSource != 'NO':
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command=['ffmpeg','-y','-f', 'rawvideo','-vcodec','rawvideo','-pix_fmt', 'bgr24',
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'-s', "{}x{}".format(outW,outH),# 图片分辨率
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'-r', str(fps),# 视频帧率
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'-i', '-','-c:v', 'libx264','-pix_fmt', 'yuv420p',
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'-f', 'flv',outSource
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]
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video_flag = videoSave['onLine']
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logdir = parAll['logChildProcessOnline']
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#print('*'*20,'###line82',command)
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else:
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video_flag = videoSave['offLine'] ;logdir = parAll['logChildProcessOffline']
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fp_log=create_logFile(logdir=logdir)
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# 管道配置，其中用到管道
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if outSource !='NO' :
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ppipe = sp.Popen(command, stdin=sp.PIPE)
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##后处理参数
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par=parAll['post_process']
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conf_thres,iou_thres,classes=par['conf_thres'],par['iou_thres'],par['classes']
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outImaDir = par['outImaDir']
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outVideoDir = par['outVideoDir']
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labelnames=par['labelnames']
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rainbows=par['rainbows']
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fpsample = par['fpsample']
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names=get_labelnames(labelnames)
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label_arraylist = get_label_arrays(names,rainbows,outfontsize=40)
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dataset = LoadStreams(inSource, img_size=640, stride=32)
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childCallback.send('####model load success####')
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if (outVideoDir!='NO') and video_flag:
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msg_id = streamName.split('-')[2]
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save_path = os.path.join(outVideoDir,msg_id+'.MP4')
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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (outW,outH))
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iframe = 0;post_results=[];time_beg=time.time()
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t00=time.time()
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time_kafka0=time.time()
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for path, img, im0s, vid_cap in dataset:
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t0= time_synchronized()
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if not path:
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EndUrl='%s/%s_frame-9999-9999_type-结束_9999999999999999_s-%s_AI.jpg'%(outImaDir,time_str(),streamName)
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EndUrl = EndUrl.replace(' ','-').replace(':','-')
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img_end=np.zeros((100,100),dtype=np.uint8);cv2.imwrite(EndUrl,img_end)
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if imageTxtFile:
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EndUrl_txt = EndUrl.replace('.jpg','.txt')
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fp_t=open(EndUrl_txt,'w');fp_t.write(EndUrl+'\n');fp_t.close()
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EndUrl='%s/%s_frame-9999-9999_type-结束_9999999999999999_s-%s_OR.jpg'%(outImaDir,time_str(),streamName)
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EndUrl = EndUrl.replace(' ','-').replace(':','-')
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ret = cv2.imwrite(EndUrl,img_end)
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if imageTxtFile:
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EndUrl_txt = EndUrl.replace('.jpg','.txt')
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fp_t=open(EndUrl_txt,'w');fp_t.write(EndUrl+'\n');fp_t.close()
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#print(EndUrl,ret)
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childCallback.send('####strem ends####')
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if (outVideoDir!='NO') and video_flag:
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vid_writer.release()
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break###断流或者到终点
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if outSource == 'NO':###如果不推流，则显示进度条
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view_bar(iframe,totalcnt,time_beg ,parIn['process_uid'] )
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###直播和离线都是1分钟发一次消息。直播发
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time_kafka1 = time.time()
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if time_kafka1 - time_kafka0 >60:
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time_kafka0 = time_kafka1
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###发送状态信息waiting
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msg = copy.deepcopy(msg_dict_off);taskId,msgId = streamName.split('-')[1:3]
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msg['msg_id']= msgId; msg
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if outSource == 'NO':
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msg['progressbar']= '%.4f'%(iframe*1.0/totalcnt)
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msg['type']=1
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else:
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msg['progressbarOn']= str(iframe)
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msg['type']=2
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msg = json.dumps(msg, ensure_ascii=False)
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try:
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record_metadata = producer.send(kafka_par['topic'], msg).get()
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outstr='%s processing send progressbar or heartBeat to kafka: taskId:%s msgId:%s send:%s'%('-'*20,taskId, msgId,msg);
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wrtiteLog(fp_log,outstr);print( outstr);
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except Exception as e:
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outstr='#######kafka ERROR when processing sending progressbar or heartBeat:, error: %s'%(str(e))
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wrtiteLog(fp_log,outstr);print( outstr);
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try:
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producer = KafkaProducer(bootstrap_servers=par['server'], value_serializer=lambda v: v.encode('utf-8')).get()
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future = producer.send(par['topic'][2], msg).get()
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except Exception as e:
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outstr='%s re-send progressbar or heartBeat kafka,processing video or stream: taskId:%s msgId:%s send:%s'%('-'*20,taskId, msgId,msg);
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wrtiteLog(fp_log,outstr);print( outstr);
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time0=time.time()
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iframe +=1
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time1=time.time()
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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timeseg0 = time.time()
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seg_pred,segstr = segmodel.eval(im0s[0] )
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timeseg1 = time.time()
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t1= time_synchronized()
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pred = model(img,augment=False)[0]
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time4 = time.time()
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datas = [path, img, im0s, vid_cap,pred,seg_pred,iframe]
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p_result,timeOut = post_process_(datas,conf_thres, iou_thres,names,label_arraylist,rainbows,iframe)
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t2= time_synchronized()
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#print('###line138:',timeOut,outSource,outVideoDir)
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##每隔 fpsample帧处理一次，如果有问题就保存图片
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if (iframe % fpsample == 0) and (len(post_results)>0) :
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parImage=save_problem_images(post_results,iframe,names,streamName=streamName,outImaDir='problems/images_tmp',imageTxtFile=imageTxtFile)
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post_results=[]
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if len(p_result[2] )>0: ##
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post_results.append(p_result)
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t3= time_synchronized()
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image_array = p_result[1]
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if outSource!='NO':
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ppipe.stdin.write(image_array.tobytes())
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if (outVideoDir!='NO') and video_flag:
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ret = vid_writer.write(image_array)
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t4= time_synchronized()
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timestr2 = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
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if iframe%100==0:
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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)
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wrtiteLog(fp_log,outstr);
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#print(outstr)
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t00 = t4;
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##模型加载之类的错误
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#except Exception as e:
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# print(time.strftime("%Y-%m-%d %H:%M:%S ", time.localtime()) ,'*'*20,'###line177 ERROR:',e)
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# childCallback.send(e) #将异常通过管道送出
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def lauch_process(gpuid,inSource,outSource,taskId,msgId,modelJson,kafka_par):
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if outSource=='NO':
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streamName='off-%s-%s'%(taskId,msgId)
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else:
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streamName='live-%s-%s'%(taskId,msgId)
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dataPar ={
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'imgData':'',
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'imgName':'testW',
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'streamName':streamName,
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'taskId':taskId,
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'msgId':msgId,
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'device':str(gpuid),
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'modelJson':modelJson,
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'kafka_par':kafka_par,
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}
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#dataPar['inSource'] = 'http://images.5gai.taauav.com/video/8bc32984dd893930dabb2856eb92b4d1.mp4';dataPar['outSource'] = None
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dataPar['inSource'] = inSource;dataPar['outSource'] = outSource
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process_uid=''.join(random.sample(string.ascii_letters + string.digits, 16));dataPar['process_uid']=process_uid
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parent_conn, child_conn = multiprocessing.Pipe();dataPar['callback']=child_conn
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gpuProcess=Process(target=onlineModelProcess,name='process:%s'%( process_uid ),args=(dataPar,))
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gpuProcess.start()
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#print(dir(gpuProcess))
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child_return = parent_conn.recv()
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timestr2=time.strftime("%Y-%m-%d %H:%M:%S ", time.localtime())
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print(timestr2,'-'*20,'progress:%s ,msgId:%s , taskId:%s return:'%(process_uid,msgId,taskId),child_return)
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return gpuProcess
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msg_dict_offline = {
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"biz_id":"hehuzhang",
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"mod_id":"ai",
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"msg_id":'bb'+''.join(random.sample(string.ascii_letters ,30) ) ,
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"offering_id":"http://vod.play.t-aaron.com/customerTrans/c49a2c620795d124f2ae4b10197b8d0e/303b7a58-17f3ef4494e-0004-f90c-f2c-7ec68.mp4",
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"offering_type":"mp4",
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"results_base_dir": "XJRW202203171535"+str(random.randint(10,99)),
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'outSource':'NO'
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}
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def detector_0(par):
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####初始化信息列表
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consumer = KafkaConsumer(
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bootstrap_servers=par['server'],
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group_id=par['group_id'],
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auto_offset_reset='earliest',
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#max_poll_interval_ms = 1000*60*6,
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#session_timeout_ms=1000*60*5,
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request_timeout_ms=15000,
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#enable_auto_commit=True
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)
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consumer.subscribe( par['topic'][0:2])
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kafka_par ={ 'server':par['server'],'topic':par['topic'][2] }
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producer = KafkaProducer(
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bootstrap_servers=par['server'],#tencent yun
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value_serializer=lambda v: v.encode('utf-8'),
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metadata_max_age_ms=120000)
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taskStatus={}
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taskStatus['onLine'] = Queue(100)
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taskStatus['offLine']= Queue(100)
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taskStatus['pidInfos']= {}
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fp_log=create_logFile(logdir=par['logDir'])
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wrtiteLog(fp_log,'###########masster starts in line222######\n')
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timeSleep=1
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#taskStatus['pidInfos'][31897]={'gpuProcess':'onlineProcess','type':'onLine'}
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time0=time.time()
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time0_kafQuery=time.time()
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time0_taskQuery=time.time()
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time0_sleep=time.time()
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time_interval=10; outStrList={}
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isleep=0
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while True:###每隔timeSleep秒，轮询一次
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#for isleep in range(1):
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##1-读取kafka，更新任务类别
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try:
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#msgs = getAllRecords(consumer,par['topic'])
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msgs=[]
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for ii,msg in enumerate(consumer):
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consumer.commit()
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msgs.append(msg)
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except Exception as e:
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outstr='%s kafka connecting error:%s '%('#'*20,e)
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outstr=wrtiteLog(fp_log,outstr);print( outstr);
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time.sleep(timeSleep)
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continue
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#if get_whether_gpuProcess():
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for it in range(30):
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timestr=time.strftime("%Y-%m-%d %H:%M:%S ", time.localtime())
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print('%s i=%d sleep:%s '%(timestr,isleep,it*10))
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time.sleep(10)
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isleep+=1
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print('########Program End#####')
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def detector(par):
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####初始化信息列表
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consumer = KafkaConsumer(
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bootstrap_servers=par['server'],
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group_id=par['group_id'],
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auto_offset_reset='earliest',
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#max_poll_interval_ms = 1000*60*6,
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#session_timeout_ms=1000*60*5,
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#request_timeout_ms=11000,
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#enable_auto_commit=True
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)
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consumer.subscribe( par['topic'][0:2])
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kafka_par ={ 'server':par['server'],'topic':par['topic'][2] }
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producer = KafkaProducer(
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bootstrap_servers=par['server'],#tencent yun
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value_serializer=lambda v: v.encode('utf-8'),
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metadata_max_age_ms=120000)
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taskStatus={}
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taskStatus['onLine'] = Queue(100)
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taskStatus['offLine']= Queue(100)
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taskStatus['pidInfos']= {}
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timeSleep=1
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#taskStatus['pidInfos'][31897]={'gpuProcess':'onlineProcess','type':'onLine'}
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time0=time.time()
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time0_kafQuery=time.time()
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time0_taskQuery=time.time()
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time0_sleep=time.time()
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time_interval=10; outStrList={}
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isleep=0
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for ii,msg in enumerate(consumer):
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try:
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taskInfos = eval(msg.value.decode('utf-8') )
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except:
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outstr='%s msg format error,value:%s,offset:%d partition:%s topic:%s'%('#'*20,msg.value,msg.offset,msg.topic,msg.topic)
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continue
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outstr='%s value:%s,offset:%d partition:%s topic:%s'%('#'*20,msg.value,msg.offset,msg.partition,msg.topic)
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print(outstr)
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def get_file():
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print("文件名 :",__file__,sys._getframe().f_lineno)
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print("函数名: ", sys._getframe().f_code.co_name)
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print("模块名: ", sys._getframe().f_back.f_code.co_name)
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if __name__ == '__main__':
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par={};
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###topic0--在线，topic1--离线
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#par['server']='212.129.223.66:9092';par['topic']=('thsw','thsw2','testReturn');par['group_id']='test';
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#101.132.127.1:19092
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'''
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par['server']='101.132.127.1:19092 ';par['topic']=('alg-online-tasks','alg-offline-tasks','alg-task-results');par['group_id']='test';
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par['kafka']='mintors/kafka'
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par['modelJson']='conf/model.json'
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'''
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masterFile="conf/master_ten.json"
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assert os.path.exists(masterFile)
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with open(masterFile,'r') as fp:
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data=json.load(fp)
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get_file()
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par=data['par']
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print(par)
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detector(par)
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