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,AI_det_track_batch_N,get_images_videos,default_mix
import cv2,os,time
from segutils.segmodel import SegModel
from yolov5 import yolov5Model
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,draw_painting_joint
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")
rainbows=[ [0,0,255],[0,255,0],[255,0,0],[255,0,255],[255,255,0],[255,129,0],[255,0,127],[127,255,0],[0,255,127],[0,127,255],[127,0,255],[255,127,255],[255,255,127],[127,255,255],[0,255,255],[255,127,255],[127,255,255],  [0,127,0],[0,0,127],[0,255,255]]
def drawAllBox(preds,imgDraw,label_arraylist,rainbows,font):
    for box in  preds:
        #cls,conf,xyxy = box[0],box[5], box[1:5] 
        #print('#'*20,'line47',box)
        cls,conf,xyxy = box[5],box[4], box[0:4] ##2023.08.03,修改了格式
        #print('#####line46 demo.py:', cls,conf,xyxy, len(label_arraylist),len(rainbows) )
        imgDraw = draw_painting_joint(xyxy,imgDraw,label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font,socre_location="leftBottom")
    return imgDraw
def get_drawPar(par):
    labelnames = par['labelnames']
    names=get_labelnames(labelnames)
    mode_paras=par['detModelpara']
    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras) 
    outfontsize=int(1080/1920*40);###
    label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['txtFontSize'],fontpath="../AIlib2/conf/platech.ttf")
    drawPar={'names':names,'label_arraylist':label_arraylist,'rainbows':rainbows,'font': par['digitFont'],'allowedList':allowedList}
    return drawPar
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'])
                retResults,timeInfos = AI_det_track_batch_N(imgarray_list, iframe_list ,par0['modelList'],par0['postProcess'],par0['sort_tracker'],par0['trackPar']) 
                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_N(business ):
    ####河道巡检的跟踪模型参数
    if opt['business'] == 'river' or opt['business'] == 'river2' or opt['business'] == 'riverT': 
        from utilsK.queRiver import riverDetSegMixProcess_N
        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
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':riverDetSegMixProcess_N,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}}, #分割和检测混合处理的函数         
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    },
                    {
                        'weight':"../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business']  ),
                        'par':{
                            'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
                        'model':stdcModel,
                        'name':'stdc'        
                    }
                        
                ],
            '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
            '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/%s'%(opt['business']),###测试图像的位置
            'testOutPath':'images/results/',###输出测试图像位置
            
        }

    if opt['business'] == 'highWay2':
        from  segutils.trafficUtils  import tracfficAccidentMixFunction_N 
        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
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':tracfficAccidentMixFunction_N,
                'pars':{ 'RoadArea': 16000, 'vehicleArea': 10, 'roadVehicleAngle': 15, 'speedRoadVehicleAngleMax': 75,'radius': 50 , 'roundness': 1.0, 'cls': 10, 'vehicleFactor': 0.1,'cls':9, 'confThres':0.25,'roadIou':0.6,'vehicleFlag':False,'distanceFlag': False }
                },
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    },
                    {
                        #'weight':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business']  ),
                        'par':{
                            '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,'seg_nclass':3},###分割模型预处理参数
                        'model':stdcModel,
                        'name':'stdc'        
                    }
                        
                ],
                

            '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
            '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
            'testImgPath':'/mnt/thsw2/DSP2/weights/highWay2/images/',
            'testOutPath':'images/results/',###输出测试图像位置
        }
        par['postProcess']['pars']['modelSize'] = par['models'][1]['par']['modelSize']
          
    if opt['business'] == 'noParking':
        from utilsK.noParkingUtils import mixNoParking_road_postprocess_N
        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
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':mixNoParking_road_postprocess_N,
                        'pars': { 'roundness': 0.3, 'cls': 9,  'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000,'fitOrder':2}
                        } ,
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    },
                    {
                        #'weight':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business']  ),
                        'par':{
                            '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,'seg_nclass':4},###分割模型预处理参数
                        'model':stdcModel,
                        'name':'stdc'        
                    }
                        
                ],
            '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
            '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['postProcess']['pars']['modelSize'] = par['models'][1]['par']['modelSize']
        
    if opt['business'] == 'drowning':
        from utilsK.drownUtils import mixDrowing_water_postprocess_N
        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
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':mixDrowing_water_postprocess_N,
                        'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    },
                    {
                        #'weight':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business']  ),
                        'par':{
                            '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,'seg_nclass':2},###分割模型预处理参数
                        'model':stdcModel,
                        'name':'stdc'        
                    }
                        
                ],
            '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    
            '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['postProcess']['pars']['modelSize'] = par['models'][1]['par']['modelSize']
        
    if opt['business'] == 'cityMangement2':
        from DMPR import DMPRModel
        from DMPRUtils.jointUtil import  dmpr_yolo_stdc

        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
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},     
            'postProcess':{
                    'function':dmpr_yolo_stdc,
                    'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80}
                    }, 
            'models':
                [
                    {
                        #'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } }
                    
                    },
                    {
                        #'weight':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        'weight':'../AIlib2/weights/conf/%s/dmpr.pth'%(opt['business']  ),
                        'par':{
                            'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.3, 'dmprimg_size':640,
                            'name':'dmpr'
                             },
                        'model':DMPRModel,     
                        'name':'dmpr'
                    }
            ]   ,
            '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
            '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'] == 'cityMangement3':
        from DMPR import DMPRModel
        from DMPRUtils.jointUtil import  dmpr_yolo_stdc
        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
                'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},       
                'postProcess':{
                    'function':dmpr_yolo_stdc,
                    'pars':{'carCls':0 ,'illCls':3,'scaleRatio':0.5,'border':80}
                    }, 
                'models':[
                        {
                            'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                            #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                            'name':'yolov5',
                            'model':yolov5Model,
                            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } }
                        
                        },
                        {
                            'weight':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###DMPR模型路径
                            #'weight':'../AIlib2/weights/conf/%s/dmpr.pth'%(opt['business']  ),
                            'par':{
                                'depth_factor':32,'NUM_FEATURE_MAP_CHANNEL':6,'dmpr_thresh':0.1, 'dmprimg_size':640,
                                'name':'dmpr'
                                 },
                            'model':DMPRModel,     
                            'name':'dmpr'
                        },
                        {
                            'weight':"../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###分割模型路径
                          
                            #'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business']  ),
                            'par':{
                                '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,'seg_nclass':2},###分割模型预处理参数
                            'model':stdcModel,
                            'name':'stdc'        
                            
                        } 
                    ],
                
                '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
                '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':'images/%s/'%(opt['business']),
                #'testImgPath':'/mnt/thsw2/DSP2/weights/cityMangement3/images/',
                'testOutPath':'images/results/',###输出测试图像位置    
            }
    
    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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                    
                        
                ],
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in []  ],
            '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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                    
                        
                ], 
            '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
            '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/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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                    
                        
                ], 
          
            '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
            '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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            '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
            '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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            '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
            '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
        }
    #单独的“坑槽”检测    
    if opt['business'] == 'pothole':
        
        par={
            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
            'labelnames':"../AIlib2/weights/conf/pothole/labelnames.json", ###检测类别对照表
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                    
                        
                ],
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in []  ],
            '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/pothole/',###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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in []  ],###控制哪些检测类别显示、输出
            
           
            '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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            '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
            '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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            '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
            '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
            'max_workers':1, ###并行线程数
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':default_mix,'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],  
            '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
            '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_N
        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
            'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
            'postProcess':{'function':illParking_postprocess_N, 'pars':{ }},
            'models':
                [
                    {
                        'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                        #'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                        'name':'yolov5',
                        'model':yolov5Model,
                        'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.25,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.25,"1":0.3,"2":0.3,"3":0.3  } },       
                    }
                ],     
            '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
            '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
        }
        
    #第一步加载模型
    modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
    #第二步准备跟踪参数
    trackPar=par['trackPar']
    sort_tracker = Sort(max_age=trackPar['sort_max_age'],
                       min_hits=trackPar['sort_min_hits'],
                       iou_threshold=trackPar['sort_iou_thresh'])
             
    #第三步准备画图字体
    drawPar = get_drawPar(par)
 
    #第四步获取图像测试及视频
    imgpaths,videopaths = get_images_videos( par['testImgPath'])

    #第五步开始测试
    for i in range(len(imgpaths)):
        imgpath = imgpaths[i]
        bname = os.path.basename(imgpath )
        im0s=[cv2.imread(imgpath)]
        time00 = time.time()

        retResults,timeOut = AI_det_track_batch_N(im0s, [i] ,modelList,par['postProcess'],sort_tracker ,trackPar)
        '''
        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[  x0 ,y0  ,x1  ,y1  ,conf,cls ,ifrmae,trackId ]
                            --etc. retResults[2][j][k]表示第j帧的第k个框。
        '''
        time11 = time.time()
        if len(retResults[1])>0:
            retResults[0][0] = drawBoxTraceSimplied(retResults[1],i, retResults[0][0],rainbows=rainbows,boxFlag=True,traceFlag=False,names=drawPar['names'])
        
        image_array = retResults[0][0]
        
        cv2.imwrite(  os.path.join( par['testOutPath'],bname ) ,image_array )
        print('----image:%s, Allprocess:%s  %s ,save:%s , objcnt:%d'%(bname,(time11-time00) * 1000, timeOut,(time.time() - time11) * 1000 ,len(retResults[2]))  )
    ##第五步开始测试视频    
    print('##begin to process videos, total %d videos'%( len(videopaths)))
    for i,video in enumerate(videopaths):
        print('process video%d :%s '%(i,video))

        par0={'modelList':modelList,'postProcess':par['postProcess'],'drawPar':drawPar,'outpth':par['testOutPath'], 'sort_tracker':sort_tracker,'trackPar':trackPar}
        
        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 customization_demo(opt):
    from AI import AI_process_C     
        
    if opt['business'] == 'channel2': 
        
        from ocr import ocrModel
        
        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
                'postProcess':{'function':default_mix,'pars':{'objs':[2] },'name':'channel2',},     
                'models':[
                        {
                            #'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                            'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                            #'weight':'/mnt/thsw2/DSP2/weights/channel2/yolov5.pt',
                            #'weight':'/mnt/thsw2/DSP2/weights/channel2/yolov5_2080Ti_fp16.engine',
                            'name':'yolov5',
                            'model':yolov5Model,
                            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':list(range(20)),'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{str(x):0.1 for x in range(20) } }
                        
                        },
                      
                        {                      
                            'weight' : '../weights/%s/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(opt['gpu'], opt['gpu']),  
                            'name':'ocr',
                            'model':ocrModel,
                            'par':{
                                'char_file':'../AIlib2/weights/conf/ocr2/chars.txt',
                                'mode':'ch',
                                'nc':3,
                                'imgH':32,
                                'imgW':192,
                                'hidden':256,
                                'mean':[0.5,0.5,0.5],
                                'std':[0.5,0.5,0.5],
                                'dynamic':False,
                                },
                        }  
                    ],
                
                '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
                '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':'images/%s/'%(opt['business']),
                #'testImgPath':'/home/thsw2/WJ/src/OCR/shipNames',
                'testOutPath':'images/results/',###输出测试图像位置    
            }
            
    if opt['business'] == 'crackMeasurement': 
        from utilsK.crackUtils import Crack_measure
        print(  '%s 只能测试图像，不能测试视频%s'%('#'*20,'#'*20))
        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
                'postProcess':{
                    'name':'crackMeasurement',
                    'function':Crack_measure,
                    'pars':{'dsx':(123-30)*1000/35*0.004387636  ,'objs':[0,1,2]}
                    }, 
                'models':[
                        {
                            #'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                            'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business']  ),
                            'name':'yolov5',
                            'model':yolov5Model,
                            'par':{ 'half':True,'device':'cuda:0' ,'conf_thres':0.1,'iou_thres':0.45,'allowedList':[0,1,2,3],'segRegionCnt':1, 'trtFlag_det':False,'trtFlag_seg':False, "score_byClass":{"0":0.1,"1":0.1,"2":0.1 } }
                        
                        },
                      
                        {                      
                            #'weight':"../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
                            'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business']  ),
                            'par':{
                                #'modelSize':(640,360),
                                'modelSize':(1920,1080),
                                'dynamic':True,
                                'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'predResize':True,'numpy':False, 'RGB_convert_first':True,'seg_nclass':2},###分割模型预处理参数
                            'model':stdcModel,
                            'name':'stdc'        
                            
                        }
                        
              
                       
                    ],
                
                '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
                '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':'images/%s/'%(opt['business']),
                #'testImgPath':'/mnt/thsw2/DSP2/weights/cityMangement2_1102/images/debug',
                'testOutPath':'images/results/',###输出测试图像位置    
            }
               
    #第一步加载模型
    modelList=[ modelPar['model'](weights=modelPar['weight'],par=modelPar['par'])  for  modelPar in par['models'] ]
    print(' load moder over')

    #准备画图字体
    labelnames = par['labelnames'] ##对应类别表
    names=get_labelnames(labelnames)
    label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['txtFontSize'],fontpath="../AIlib2/conf/platech.ttf")

    #图像测试
    imgpaths,videopaths = get_images_videos( par['testImgPath'])


    #开始测试
    for imgUrl in   imgpaths[0:]:
        img = cv2.imread(imgUrl);bname = os.path.basename(imgUrl)
        if opt['business'] == 'crackMeasurement':
            ret,timeInfos = AI_process_C([img],modelList,par['postProcess'])
            #返回类型ret-list,[[ x0,y0,x1,y1,score,class,裂缝长度,平均宽度,最大宽度,最小宽度],[...],[...]]
            for re in ret:
                print('Summarized Cracklength = %.1f mm Mean crack width = %.1f mm  Max crack width = %.1f mm Min crack width = %.1f mm '%( re[6], re[7],re[8],re[9] ) )
        elif opt['business'] == 'channel2':
            ret,timeInfos = AI_process_C([img],modelList,par['postProcess'])
        
        timeInfos=bname+':'+timeInfos
        print(timeInfos,ret )
        if len(ret)>0:
            img0 = drawAllBox(ret,img,label_arraylist,rainbows,par['digitFont'])
        else:  img0=    img
        cv2.imwrite(os.path.join('images/results/',bname ) ,img0)

        
        
    
    
                
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",'cityMangement3','crackMeasurement','pothole','channel2','riverT']
    businessAll = [ 'forest2'     ]
    
    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)   
        elif opt['business'] in ['crackMeasurement','channel2'] :
            customization_demo(opt)
        else:
        #if opt['business'] in ['river','highWay2','noParking','drowning','']:
            det_track_demo_N(opt )