AIdemo2/demo3.0.py

import sys, yaml
from easydict import EasyDict as edict
from concurrent.futures import ThreadPoolExecutor

sys.path.extend(['..','../AIlib2' ])

from AI import AI_process,AI_process_forest,get_postProcess_para,get_postProcess_para_dic,ocr_process,AI_det_track,AI_det_track_batch
import cv2,os,time
from segutils.segmodel import SegModel

from stdc import stdcModel
from segutils.trafficUtils import tracfficAccidentMixFunction
from models.experimental import attempt_load
from utils.torch_utils import select_device
from utilsK.queRiver import get_labelnames,get_label_arrays,save_problem_images,riverDetSegMixProcess
from ocrUtils.ocrUtils import CTCLabelConverter,AlignCollate
from trackUtils.sort import Sort,track_draw_boxAndTrace,track_draw_trace_boxes,moving_average_wang,drawBoxTraceSimplied
from trackUtils.sort_obb import OBB_Sort,obbTohbb,track_draw_all_boxes,track_draw_trace
from obbUtils.shipUtils import OBB_infer,OBB_tracker,draw_obb,OBB_tracker_batch
from utilsK.noParkingUtils import mixNoParking_road_postprocess
from obbUtils.load_obb_model import load_model_decoder_OBB
import numpy as np
import torch,glob
import tensorrt as trt
from utilsK.masterUtils import get_needed_objectsIndex
from copy import deepcopy
from scipy import interpolate
from utilsK.drownUtils import mixDrowing_water_postprocess
#import warnings
#warnings.filterwarnings("error")

def view_bar(num, total,time1,prefix='prefix'):
    rate = num / total
    time_n=time.time()
    rate_num = int(rate * 30)
    rate_nums = np.round(rate * 100)
    r = '\r %s %d / %d [%s%s] %.2f s'%(prefix,num,total,  ">" * rate_num, " " * (30 - rate_num), time_n-time1 )
    sys.stdout.write(r)
    sys.stdout.flush()
        

'''
    多线程
'''

def process_v1(frame):
    #try:
    print('demo.py beging to :',frame[8])
    time00 = time.time()
    H,W,C = frame[0][0].shape
    
    p_result,timeOut = AI_process(frame[0],frame[1],frame[2],frame[3],frame[4],frame[5],objectPar=frame[6],font=frame[7],segPar=frame[9],mode=frame[10],postPar=frame[11])
     
    time11 = time.time()
    image_array = p_result[1]
    
    cv2.imwrite(os.path.join('images/results/',frame[8] ) ,image_array)
    bname = frame[8].split('.')[0]
    if len(p_result)==5:
        image_mask = p_result[4]
        cv2.imwrite(os.path.join('images/results/',bname+'_mask.png' ) , (image_mask).astype(np.uint8))
    
    boxes=p_result[2]
    with open(  os.path.join('images/results/',bname+'.txt' ),'w'  ) as fp:
        for box in boxes:
            box_str=[str(x) for x in box]
            out_str=','.join(box_str)+'\n'
            fp.write(out_str)
    time22 = time.time()
    print('%s,%d*%d,AI-process: %.1f,image save:%.1f , %s'%(frame[8],H,W, (time11 - time00) * 1000.0, (time22-time11)*1000.0,timeOut), boxes)
    return 'success'
    #except Exception as e:
    #    return 'failed:'+str(e)        
def process_video(video,par0,mode='detSeg'):
    cap=cv2.VideoCapture(video)
    if not  cap.isOpened():
        print('#####error url:',video)
        return False  
    bname=os.path.basename(video).split('.')[0]        
    fps = int(cap.get(cv2.CAP_PROP_FPS)+0.5)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH )+0.5)
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)+0.5)
    framecnt=int(cap.get(7)+0.5)
    save_path_AI = os.path.join(par0['outpth'],os.path.basename(video))
    problem_image_dir= os.path.join(  par0['outpth'], 'probleImages'   )
    os.makedirs(problem_image_dir,exist_ok=True)
    vid_writer_AI = cv2.VideoWriter(save_path_AI, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width,height))
    num=0
    iframe=0;post_results=[];fpsample=30*10
    
    imgarray_list = []; iframe_list = []
    
    patch_cnt = par0['trackPar']['patchCnt']
    ##windowsize 对逐帧插值后的结果做平滑，windowsize为平滑的长度,没隔det_cnt帧做一次跟踪。
    trackPar={'det_cnt':10,'windowsize':29 }


    ##track_det_result_update= np.empty((0,8)) ###每100帧跑出来的结果，放在track_det_result_update，只保留当前100帧里有的tracker Id.
    while cap.isOpened():
        ret, imgarray = cap.read() #读取摄像头画面
        iframe +=1
        if not ret:break
        if mode=='detSeg':
            p_result,timeOut = AI_process([imgarray],par0['model'],par0['segmodel'],par0['names'],par0['label_arraylist'],par0['rainbows'],objectPar=par0['objectPar'],font=par0['digitFont'],segPar=par0['segPar'])
        elif mode == 'track':
            #sampleCount=10
            imgarray_list.append( imgarray )
            iframe_list.append(iframe )    
            if iframe%patch_cnt==0:
                time_patch0 = time.time()
                retResults,timeInfos = AI_det_track_batch(imgarray_list, iframe_list ,par0['modelPar'],par0['processPar'],par0['sort_tracker'] ,par0['trackPar'],segPar=par0['segPar'])
                #print('###line111:',retResults[2])
                ###需要保存成一个二维list，每一个list是一帧检测结果。
                ###track_det_result 内容格式：x1, y1, x2, y2, conf, cls,iframe,trackId
                time_patch2 = time.time()                    
                frame_min = iframe_list[0];frame_max=iframe_list[-1]
                for iiframe in range(frame_min,frame_max+1):
                    img_draw = imgarray_list[ iiframe- frame_min  ]
                    img_draw = drawBoxTraceSimplied(retResults[1] ,iiframe, img_draw,rainbows=par0['drawPar']['rainbows'],boxFlag=True,traceFlag=True,names=par0['drawPar']['names'] )
                    ret = vid_writer_AI.write(img_draw)        
                    view_bar(iiframe, framecnt,time.time(),prefix=os.path.basename(video))
                imgarray_list=[];iframe_list=[]     
        elif mode =='obbTrack':
            imgarray_list.append( imgarray )
            iframe_list.append(iframe )    
            if iframe%patch_cnt==0:
                time_patch0 = time.time()
                    
                track_det_results,  timeInfos = OBB_tracker_batch(imgarray_list,iframe_list,par0['modelPar'],par0['obbModelPar'],par0['sort_tracker'],par0['trackPar'],segPar=None)
                print(  timeInfos )
                
                #对结果画图
                track_det_np = track_det_results[1]
                frame_min = iframe_list[0];frame_max=iframe_list[-1]
                for iiframe in range(frame_min,frame_max+1):
                    img_draw = imgarray_list[ iiframe- frame_min  ]
                    
                    if len( track_det_results[2][  iiframe- frame_min]) > 0:
                        img_draw =  draw_obb( track_det_results[2][iiframe- frame_min ] ,img_draw,par0['drawPar'])
                        if  True: 
                            frameIdex=12;trackIdex=13;
                            boxes_oneFrame = track_det_np[ track_det_np[:,frameIdex]==iiframe   ]
                            
                            ###在某一帧上，画上轨迹
                            track_ids = boxes_oneFrame[:,trackIdex].tolist()
                            boxes_before_oneFrame = track_det_np[ track_det_np[:,frameIdex]<=iiframe   ]
                            for trackId in track_ids:
                                boxes_before_oneFrame_oneId = boxes_before_oneFrame[boxes_before_oneFrame[:,trackIdex]==trackId]
                                xcs = boxes_before_oneFrame_oneId[:,8]
                                ycs = boxes_before_oneFrame_oneId[:,9]
                                [cv2.line(img_draw, (  int(xcs[i]) , int(ycs[i]) ), 
                                    ( int(xcs[i+1]),int(ycs[i+1])  ),(255,0,0), thickness=2) 
                                    for i,_ in  enumerate(xcs)  if i < len(xcs)-1 ]

                    ret = vid_writer_AI.write(img_draw)   
                   
                #sys.exit(0)
                #print('vide  writer ret:',ret)
                imgarray_list=[];iframe_list=[]     
  
            view_bar(iframe, framecnt,time.time(),prefix=os.path.basename(video))    
            
        else:
            p_result,timeOut = AI_process_forest([imgarray],par0['model'],par0['segmodel'],par0['names'],par0['label_arraylist'],par0['rainbows'],par0['half'],par0['device'],par0['conf_thres'], par0['iou_thres'],par0['allowedList'],font=par0['digitFont'],trtFlag_det=par0['trtFlag_det'])
            

        if  mode not in [ 'track','obbTrack']:   
            image_array = p_result[1];num+=1
            ret = vid_writer_AI.write(image_array)    
            view_bar(num, framecnt,time.time(),prefix=os.path.basename(video))
            ##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,par0['names'],streamName=bname,outImaDir=problem_image_dir,imageTxtFile=False)
                post_results=[]   
    
            if len(p_result[2] )>0:                
                post_results.append(p_result)
 
    vid_writer_AI.release();       
      
def det_track_demo(business ):
    '''
    跟踪参数说明：
    'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100} 
        sort_max_age--跟踪链断裂时允许目标消失最大的次数。超过之后，会认为是新的目标。
        sort_min_hits--每隔目标连续出现的次数，超过这个次数才认为是一个目标。
        sort_iou_thresh--检测最小的置信度。
        det_cnt--每隔几次做一个跟踪和检测，默认10。
        windowsize--轨迹平滑长度，一定是奇数，表示每隔几帧做一平滑，默认29。
        patchCnt--每次送入图像的数量，不宜少于100帧。
    '''
    
    ''' 以下是基于检测和分割的跟踪模型，分割用来修正检测的结果'''        
    ####河道巡检的跟踪模型参数
    if opt['business'] == 'river' or opt['business'] == 'river2' : 
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
            'gpuname':'2080Ti',###显卡名称
            'max_workers':1, ###并行线程数
            'half':True,
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            
            'seg_nclass':2,###分割模型类别数目，默认2类
            'segRegionCnt':0,###分割模型结果需要保留的等值线数目
            
            'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,#分割模型预处理参数
                        'mixFunction':{'function':riverDetSegMixProcess,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}} #分割和检测混合处理的函数 
                        },
            
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            
            'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/%s/para.json'%( opt['business'] ),###后处理参数文件
            'txtFontSize':80,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            #'testImgPath':'images/videos/river',###测试图像的位置
            'testImgPath':'images/tt',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
            
        }
    
    if opt['business'] == 'highWay2':
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(  opt['business']  ), ###检测类别对照表
            'half':True,
            'gpuname':'3090',###显卡名称
            'max_workers':1, ###并行线程数
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            #'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
            'seg_nclass':3,###分割模型类别数目，默认2类
            'segRegionCnt':2,###分割模型结果需要保留的等值线数目
            'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
                      'mixFunction':{'function':tracfficAccidentMixFunction,
                        'pars':{ 'RoadArea': 16000, 'vehicleArea': 10, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75,'radius': 50 , 'roundness': 1.0, 'cls': 9, 'vehicleFactor': 0.1,'cls':9, 'confThres':0.25,'roadIou':0.6,'vehicleFlag':False,'distanceFlag': False }
                        }
                    },

            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},
            'mode':'highWay3.0',
            'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
            'txtFontSize':20,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':0.5,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
            #'testImgPath':'images/trafficAccident/8.png',###测试图像的位置
            'testImgPath':'/home/chenyukun/777-7-42.mp4',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
        par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
    if opt['business'] == 'noParking':
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(  opt['business']  ), ###检测类别对照表
            'half':True,
            'gpuname':'3090',###显卡名称
            'max_workers':1, ###并行线程数
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            #'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
            'seg_nclass':4,###分割模型类别数目，默认2类
            'segRegionCnt':2,###分割模型结果需要保留的等值线数目
            'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
                      'mixFunction':{'function':mixNoParking_road_postprocess,
                        'pars': { 'roundness': 0.3, 'cls': 9,  'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000,'fitOrder':2}
                        }    
                    },
                    
                    

            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'mode':'highWay3.0',
            'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/%s/para.json'%('highWay2' ),###后处理参数文件
            'txtFontSize':20,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
            'testImgPath':'images/noParking/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
        par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
    
    if opt['business'] == 'cityMangement2':
        from DMPR import DMPRModel
        from DMPRUtils.jointUtil import  dmpr_yolo

        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
            'max_workers':1, ###并行线程数
            'half':True,
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            #'Detweights':"/mnt/thsw2/DSP2/weights/cityMangement2/weights/urbanManagement/yolo/best.pt",
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
            'seg_nclass':4,###分割模型类别数目，默认2类
            'segRegionCnt':2,###分割模型结果需要保留的等值线数目

            'segPar':{ 'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640,
                      'mixFunction':{'function':dmpr_yolo,
                        'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80}
                        }
                    },
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},       
            #'Segweights' : '/mnt/thsw2/DSP2/weights/cityMangement2/weights/urbanManagement/DMPR/dp_detector_499.engine',###分割模型权重位置
            'Segweights':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
            'txtFontSize':20,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
            #'testImgPath':'/mnt/thsw2/DSP2/demoImages/illParking',###测试图像的位置
            'testImgPath':'/mnt/thsw2/DSP2/weights/cityMangement2_0916/images/input',
            #'testImgPath':'images/cityMangement/',
            'testOutPath':'images/results/',###输出测试图像位置
        }
    if opt['business'] == 'drowning':
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(  opt['business']  ), ###检测类别对照表
            'half':True,
            'gpuname':'3090',###显卡名称
            'max_workers':1, ###并行线程数
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            #'Detweights':"../AIlib2/weights/conf/highWay2/yolov5.pt",
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
            'seg_nclass':2,###分割模型类别数目，默认2类
            'segRegionCnt':2,###分割模型结果需要保留的等值线数目
            'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,###分割模型预处理参数
                      'mixFunction':{'function':mixDrowing_water_postprocess,
                        'pars':{ }
                        }
                    },

            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/%s/para.json'%('highWay2' ),###后处理参数文件
            'txtFontSize':20,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'segLineShow':True,'waterLineWidth':2},###显示框、线设置
            'testImgPath':'images/drowning/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
        par['segPar']['mixFunction']['pars']['modelSize'] = par['segPar']['modelSize']
    
          
    ''' 以下是基于检测的跟踪模型，只有检测没有分割 '''
    if opt['business'] == 'forest2':
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/forest2/labelnames.json", ###检测类别对照表
            'gpuname':opt['gpu'],###显卡名称
            'max_workers':1, ###并行线程数
            'half':True,
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            #'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in []  ],
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###分割模型类别数目，默认2类
            'segRegionCnt':0,###分割模型结果需要保留的等值线数目
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/forest/para.json',###后处理参数文件
            'txtFontSize':80,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'../AIdemo2/images/forest2/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
        
    ###车辆巡检参数
    if opt['business'] == 'vehicle': 
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/vehicle/labelnames.json", ###检测类别对照表
            'gpuname':'2080T',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###分割模型类别数目，默认2类
            'segRegionCnt':0,###分割模型结果需要保留的等值线数目
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/vehicle/para.json',###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'images/videos/vehicle/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
    ###行人检测模型
    if opt['business'] == 'pedestrian': 
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/pedestrian/labelnames.json", ###检测类别对照表
            'gpuname':'2080T',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###分割模型类别数目，默认2类
            'segRegionCnt':0,###分割模型结果需要保留的等值线数目
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/pedestrian/para.json',###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'../AIdemo2/images/pedestrian/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
    if opt['business'] == 'smogfire': 
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/smogfire/labelnames.json", ###检测类别对照表
            'gpuname':'2080T',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###没有分割模型，此处不用
            'segRegionCnt':0,###没有分割模型，此处不用
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/smogfire/para.json',###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'../AIdemo2/images/smogfire/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
    ###钓鱼游泳检测
    if opt['business'] == 'AnglerSwimmer': 
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/AnglerSwimmer/labelnames.json", ###检测类别对照表
            'gpuname':'2080T',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###没有分割模型，此处不用
            'segRegionCnt':0,###没有分割模型，此处不用
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/AnglerSwimmer/para.json',###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'../AIdemo2/images/AnglerSwimmer/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
    ###航道应急，做落水人员检测, channelEmergency    
    if opt['business'] == 'channelEmergency': 
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/channelEmergency/labelnames.json", ###检测类别对照表
            'gpuname':'2080T',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            #'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in []  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###没有分割模型，此处不用
            'segRegionCnt':0,###没有分割模型，此处不用
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/channelEmergency/para.json',###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'../AIdemo2/images/channelEmergency/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
   
   ###乡村路违法种植
    if opt['business'] == 'countryRoad': 
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/countryRoad/labelnames.json", ###检测类别对照表
            'gpuname':'2080T',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###没有分割模型，此处不用
            'segRegionCnt':0,###没有分割模型，此处不用
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/countryRoad/para.json',###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'../AIdemo2/images/countryRoad/',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
   
    ###城管项目，检测城市垃圾和车辆
    if opt['business'] == 'cityMangement':
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
            'gpuname':'2080Ti',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###没有分割模型，此处不用
            'segRegionCnt':0,###没有分割模型，此处不用
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business']),###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'images/cityMangement',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
    ###城管项目，检测道路情况，输入类别为五个："护栏","交通标志","非交通标志","施工","施工“(第4，第5类别合并，名称相同)
    ###实际模型检测输出的类别为："护栏","交通标志","非交通标志","锥桶","水马"    
    if opt['business'] == 'cityRoad':
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
            'gpuname':'2080Ti',###显卡名称
            'half':True,
            'max_workers':1, ###并行线程数
            'trtFlag_det':True,###检测模型是否采用TRT
            'trtFlag_seg':False,###分割模型是否采用TRT
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':2,###没有分割模型，此处不用
            'segRegionCnt':0,###没有分割模型，此处不用
            'segPar':None,###分割模型预处理参数
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business']),###后处理参数文件
            'txtFontSize':40,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
            'testImgPath':'images/%s'%(opt['business']),###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
        
    if opt['business'] == 'illParking':
        from utilsK.illParkingUtils import illParking_postprocess
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'half':True,
            'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
            'max_workers':1, ###并行线程数
            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,5,6,7,8,9]  ],###控制哪些检测类别显示、输出
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'seg_nclass':4,###没有分割模型，此处不用
            'segRegionCnt':2,###没有分割模型，此处不用
       
            'segPar':{
                      'mixFunction':{'function':illParking_postprocess,
                        'pars':{ }
                        }
                    },
                    
            'Segweights' : None,###分割模型权重位置
            'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
            'txtFontSize':20,###文本字符的大小
            'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':2},###显示框、线设置
            'testImgPath':'images/cityMangement',###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
        }
        
        
    par['trtFlag_det']=True if par['Detweights'].endswith('.engine') else False
    if par['Segweights']:
        par['segPar']['trtFlag_seg']=True if  par['Segweights'].endswith('.engine') else False
        
    ##使用森林，道路模型,business 控制['forest','road']
    ##预先设置的参数
    #gpuname=par['gpuname']#如果用trt就需要此参数，只能是"3090" "2080Ti"
    device_=par['device']  ##选定模型，可选 cpu,'0','1'
    
    
    device = select_device(device_)
    half = device.type != 'cpu'  # half precision only supported on CUDA
    trtFlag_det=par['trtFlag_det'] ###是否采用TRT模型加速
    
    ##以下参数目前不可改
    imageW=1080     ####道路模型
    digitFont= par['digitFont']
    
    
    ####加载检测模型
    if trtFlag_det:
        Detweights=par['Detweights']
        logger = trt.Logger(trt.Logger.ERROR)
        with open(Detweights, "rb") as f, trt.Runtime(logger) as runtime:
            model=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
        print('####load TRT model :%s'%(Detweights))    
    else:
        Detweights=par['Detweights']
        model = attempt_load(Detweights, map_location=device)  # load FP32 model
        if half: model.half()

    ####加载分割模型
    seg_nclass = par['seg_nclass']
    segPar=par['segPar']
    if par['Segweights']:
        if opt['business'] == 'cityMangement2':
            segmodel = DMPRModel(weights=par['Segweights'], par = par['segPar'])
        else:
            segmodel = stdcModel(weights=par['Segweights'], par = par['segPar'])
        '''    
        if par['segPar']['trtFlag_seg']:
            Segweights = par['Segweights']
            logger = trt.Logger(trt.Logger.ERROR)
            with open(Segweights, "rb") as f, trt.Runtime(logger) as runtime:
                segmodel=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
            print('############locad seg model trt success: ',Segweights)
        else:
            Segweights = par['Segweights']
            segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
            print('############locad seg model pth success:',Segweights)
        '''    
    else:
        segmodel=None
        

    trackPar=par['trackPar']
    sort_tracker = Sort(max_age=trackPar['sort_max_age'],
                       min_hits=trackPar['sort_min_hits'],
                       iou_threshold=trackPar['sort_iou_thresh'])
                       
        
    labelnames = par['labelnames']
    postFile= par['postFile']
    print( Detweights,labelnames  ) 
    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
    
    detPostPar = get_postProcess_para_dic(postFile)
    conf_thres,iou_thres,classes,rainbows =  detPostPar["conf_thres"],detPostPar["iou_thres"],detPostPar["classes"],detPostPar["rainbows"]
    if 'ovlap_thres_crossCategory' in detPostPar.keys(): iou2nd=detPostPar['ovlap_thres_crossCategory']
    else:iou2nd = None
    if 'score_byClass' in detPostPar.keys(): score_byClass=detPostPar['score_byClass']
    else: score_byClass = None
    
    
    
    ####模型选择参数用如下：
    mode_paras=par['detModelpara']
    
    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)    
    #slopeIndex = par['slopeIndex']
    ##只加载检测模型，准备好显示字符
    
    names=get_labelnames(labelnames)
    #imageW=4915;###默认是1920，在森林巡检的高清图像中是4920
    outfontsize=int(imageW/1920*40);###
    label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['txtFontSize'],fontpath="../AIlib2/conf/platech.ttf")


    
    
    ##图像测试和视频
    outpth = par['testOutPath'] 
    impth = par['testImgPath']
    imgpaths=[]###获取文件里所有的图像
    videopaths=[]###获取文件里所有的视频
    img_postfixs = ['.jpg','.JPG','.PNG','.png'];
    vides_postfixs= ['.MP4','.mp4','.avi']
    if os.path.isdir(impth):
        for postfix in img_postfixs:
            imgpaths.extend(glob.glob('%s/*%s'%(impth,postfix ))  )
        for postfix in ['.MP4','.mp4','.avi']:
            videopaths.extend(glob.glob('%s/*%s'%(impth,postfix ))  )
    else:
        postfix = os.path.splitext(impth)[-1]
        if postfix in img_postfixs: imgpaths=[ impth ]
        if postfix in vides_postfixs: videopaths = [impth ]

        
        
        
    imgpaths.sort()  
    


    modelPar={ 'det_Model': model,'seg_Model':segmodel   }

    processPar={'half':par['half'],'device':device,'conf_thres':conf_thres,'iou_thres':iou_thres,'trtFlag_det':trtFlag_det,'iou2nd':iou2nd,'score_byClass':score_byClass}
    drawPar={'names':names,'label_arraylist':label_arraylist,'rainbows':rainbows,'font': par['digitFont'],'allowedList':allowedList}

    for i in range(len(imgpaths)):
    #for i in range(2):
        #imgpath = os.path.join(impth, folders[i])
        imgpath = imgpaths[i]
        bname = os.path.basename(imgpath )
        im0s=[cv2.imread(imgpath)]
        time00 = time.time()
        retResults,timeOut = AI_det_track_batch(im0s, [i] ,modelPar,processPar,sort_tracker ,trackPar,segPar)
        #print('###line627:',retResults[2])
        #retResults,timeInfos = AI_det_track_batch(imgarray_list, iframe_list ,par0['modelPar'],par0['processPar'],par0['sort_tracker'] ,par0['trackPar'],segPar=par0['segPar'])
        if len(retResults[1])>0:
            retResults[0][0] = drawBoxTraceSimplied(retResults[1],i, retResults[0][0],rainbows=rainbows,boxFlag=True,traceFlag=False,names=drawPar['names'])
        time11 = time.time()
        image_array = retResults[0][0]
        '''
        
        返回值retResults[2] --list,其中每一个元素为一个list，表示每一帧的检测结果，每一个结果是由多个list构成，每个list表示一个框，格式为[ cls , x0 ,y0  ,x1  ,y1  ,conf,ifrmae,trackId ]
                            --etc. retResults[2][j][k]表示第j帧的第k个框。
        '''
        cv2.imwrite(  os.path.join( outpth,bname ) ,image_array )
        
        print('----image:%s, process:%s ( %s ),save:%s'%(bname,(time11-time00) * 1000, timeOut,(time.time() - time11) * 1000)  )
        
    ##process video

    print('##begin to process videos, total %d videos'%( len(videopaths)))
    for i,video in enumerate(videopaths):
        print('process video%d :%s '%(i,video))
        par0={'modelPar':modelPar,'processPar':processPar,'drawPar':drawPar,'outpth':par['testOutPath'], 'sort_tracker':sort_tracker,'trackPar':trackPar,'segPar':segPar}
        process_video(video,par0,mode='track')  
        
      

def OCR_demo2(opt):
    from ocrUtils2 import crnn_model
    from ocrUtils2.ocrUtils import get_cfg,recognition_ocr,strLabelConverter
    
    if opt['business'] == 'ocr2':
         par={
            'image_dir':'images/ocr_en',
            'outtxt':'images/results',
            'weights':'../AIlib2/weights/conf/ocr2/crnn_448X32.pth',

            #'weights':'../weights/2080Ti/AIlib2/ocr2/crnn_2080Ti_fp16_448X32.engine',
            'device':'cuda:0',
            'cfg':'../AIlib2/weights/conf/ocr2/360CC_config.yaml',
            
            'char_file':'../AIlib2/weights/conf/ocr2/chars.txt',
            'imgH':32,
            'imgW':448,
            'workers':1

            
        }  
    image_dir=par['image_dir']
    outtxt=par['outtxt']
    workers=par['workers']
    weights= par['weights']
    device=par['device']

    char_file=par['char_file']
    imgH=par['imgH']
    imgW=par['imgW']
    cfg = par['cfg']
    
   
    
    config = get_cfg(cfg, char_file)    


    par['contextFlag']=False
    device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') 
    if weights.endswith('.pth'):
        model = crnn_model.get_crnn(config,weights=weights).to(device)
        par['model_mode']='pth'
    else:    
        logger = trt.Logger(trt.Logger.ERROR)
        with open(weights, "rb") as f, trt.Runtime(logger) as runtime:
            model = runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
            print('#####load TRT file:',weights,'success #####')
        context = model.create_execution_context() 
        par['model_mode']='trt';par['contextFlag']=context

    converter = strLabelConverter(config.DATASET.ALPHABETS)

    img_urls=glob.glob('%s/*.jpg'%( image_dir ))
    img_urls.extend( glob.glob('%s/*.png'%( image_dir ))  )
    cnt=len(img_urls)
    print('%s  has %d images'%(image_dir ,len(img_urls) )  )
    # 准备数据
    parList=[]
    for i in range(cnt):
        img_patch=cv2.imread( img_urls[i] ,   cv2.IMREAD_GRAYSCALE) 
        started = time.time()
        img = cv2.imread(img_urls[i])
        sim_pred = recognition_ocr(config, img, model, converter, device,par=par)
        finished = time.time()
        print('{0}: elapsed time: {1} prd:{2} '.format( os.path.basename( img_urls[i] ), finished - started, sim_pred ))
        

    
    
                
def OBB_track_demo(opt):
    ###倾斜框（OBB）的ship目标检测
    par={
     
        'obbModelPar':{
            'model_size':(608,608),'K':100,'conf_thresh':0.3, 'down_ratio':4,'num_classes':15,'dataset':'dota',
            'heads': {'hm': None,'wh': 10,'reg': 2,'cls_theta': 1},
            'mean':(0.5, 0.5, 0.5),'std':(1, 1, 1), 'half': False,'decoder':None,
            'weights':'../weights/%s/AIlib2/%s/obb_608X608_%s_fp16.engine'%(opt['gpu'],opt['business'],opt['gpu']),
            
        },
        'outpth': 'images/results',
        'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
        'device':"cuda:0",
        
        #'test_dir': '/mnt/thsw2/DSP2/videos/obbShips/DJI_20230208110806_0001_W_6M.MP4',
        'test_dir':'/mnt/thsw2/DSP2/videos/obbShips/freighter2.mp4',
        
        'test_flag':True,
        'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
        'drawBox':True,#####是否画框
        'drawPar': { 'digitWordFont' :{'line_thickness':2,'boxLine_thickness':1,'wordSize':40, 'fontSize':1.0,'label_location':'leftTop'}} ,  
        'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business'] ), ###检测类别对照表
    }
    #par['model_size'],par['mean'],par['std'],par['half'],par['saveType'],par['heads'],par['labelnames'],par['decoder'],par['down_ratio'],par['drawBox']
    #par['rainbows'],par['label_array'],par['digitWordFont']

    obbModelPar = par['obbModelPar']
    ####加载模型
    model,decoder2=load_model_decoder_OBB(obbModelPar)
    obbModelPar['decoder']=decoder2

    names=get_labelnames(par['labelnames']);obbModelPar['labelnames']=names
    
    _,_,_,rainbows=get_postProcess_para(par['postFile']);par['drawPar']['rainbows']=rainbows

    
    label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['drawPar']['digitWordFont']['wordSize'],fontpath="../AIlib2/conf/platech.ttf")
    #par['label_array']=label_arraylist

    trackPar=par['trackPar']
    sort_tracker = OBB_Sort(max_age=trackPar['sort_max_age'],
                       min_hits=trackPar['sort_min_hits'],
                       iou_threshold=trackPar['sort_iou_thresh'])
                       
    
     ##图像测试和视频
    impth = par['test_dir']
    img_urls=[]###获取文件里所有的图像
    video_urls=[]###获取文件里所有的视频
    img_postfixs = ['.jpg','.JPG','.PNG','.png'];
    vides_postfixs= ['.MP4','.mp4','.avi']
    if os.path.isdir(impth):
        for postfix in img_postfixs:
            img_urls.extend(glob.glob('%s/*%s'%(impth,postfix ))  )
        for postfix in ['.MP4','.mp4','.avi']:
            video_urls.extend(glob.glob('%s/*%s'%(impth,postfix ))  )
    else:
        postfix = os.path.splitext(impth)[-1]
        if postfix in img_postfixs: img_urls=[ impth ]
        if postfix in vides_postfixs: video_urls = [impth ]
        
    parIn = {'obbModelPar':obbModelPar,'modelPar':{'obbmodel': model},'sort_tracker':sort_tracker,'outpth':par['outpth'],'trackPar':trackPar,'drawPar':par['drawPar']}
    par['drawPar']['label_array']=label_arraylist        
    for img_url in img_urls:
        #print(img_url)
        ori_image=cv2.imread(img_url)     
        
        #ori_image_list,infos = OBB_infer(model,ori_image,obbModelPar)         
        
        ori_image_list,infos  = OBB_tracker_batch([ori_image],[0],parIn['modelPar'],parIn['obbModelPar'],None,parIn['trackPar'],None)
     
        ori_image_list[1] =  draw_obb(ori_image_list[2] ,ori_image_list[1],par['drawPar'])
        
        imgName = os.path.basename(img_url)
        saveFile = os.path.join(par['outpth'], imgName)
        ret=cv2.imwrite(saveFile, ori_image_list[1])
        if not ret:
            print(saveFile, ' not created ')
        print(  os.path.basename(img_url),':',infos,ori_image_list[2])
        
    ###处理视频
    
    for video_url in video_urls:
        process_video(video_url, parIn ,mode='obbTrack')

def crowd_demo(opt):
    if opt['business']=='crowdCounting':
     
        from crowd import crowdModel as Model
        par={
            'mean':[0.485, 0.456, 0.406], 'std':[0.229, 0.224, 0.225],'threshold':0.5, 
            'input_profile_shapes':[(1,3,256,256),(1,3,1024,1024),(1,3,2048,2048)],
            'modelPar':{'backbone':'vgg16_bn', 'gpu_id':0,'anchorFlag':False, 'width':None,'height':None ,'line':2, 'row':2},

            'weights':"../weights/%s/AIlib2/%s/crowdCounting_%s_dynamic.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
            'testImgPath':'images/%s'%(opt['business'] ),###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
            
            }
     
    
    #weights='weights/best_mae.pth'
    cmodel = Model(par['weights'],par)

    img_path = par['testImgPath']
    File = os.listdir(img_path)
    targetList = []
    for file in File[0:]:
        COORlist = []
        imgPath = img_path + os.sep + file
        
        
        img_raw = cv2.cvtColor(cv2.imread(imgPath),cv2.COLOR_BGR2RGB)
        # cmodel.eval---
        # 输入读取的RGB数组
        # 输出:list,0--原图，1-人头坐标list,2-对接OBB的格式数据，其中4个坐标均相同，2-格式如下：
        # [ [ [ (x0,y0),(x1,y1),(x2,y2),(x3,y3)  ],score， cls ],  [ [ (x0,y0),(x1,y1),(x2,y2),(x3,y3)  ],score ，cls ],........  ]
        
        prets, infos = cmodel.eval(img_raw)
        
        
        print(file,infos,' 人数：',len(prets[1]))
        
 
        img_to_draw = cv2.cvtColor(np.array(img_raw), cv2.COLOR_RGB2BGR)
        # 打印预测图像中人头的个数
        for p in prets[1]:
            img_to_draw = cv2.circle(img_to_draw, (int(p[0]), int(p[1])), 2, (0, 255, 0), -1)
            COORlist.append((int(p[0]), int(p[1])))
        # 将各测试图像中的人头坐标存储在targetList中, 格式：[[(x1, y1),(x2, y2),...], [(X1, Y1),(X2, Y2),..], ...]
        targetList.append(COORlist)
        #time.sleep(2)
        # 保存预测图片
        cv2.imwrite(os.path.join(par['testOutPath'], file), img_to_draw)
 
if __name__=="__main__":

    #jkm_demo()
    businessAll=['river', 'river2','highWay2','noParking','drowning','forest2','vehicle','pedestrian','smogfire' , 'AnglerSwimmer','channelEmergency', 'countryRoad','cityMangement','ship2','cityMangement2','cityRoad','illParking',"crowdCounting"]
    businessAll = ['crowdCounting']
    
    for busi in businessAll:
        print('-'*40,'beg to test:',busi,'-'*40)
        opt={'gpu':'2080Ti','business':busi}
        if busi in ['ship2']:
            OBB_track_demo(opt)
        elif  opt['business'] in ['crowdCounting'] :
            crowd_demo(opt)    
        else:
        #if opt['business'] in ['river','highWay2','noParking','drowning','']:
            det_track_demo(opt )