1141 lines
67 KiB
Python
1141 lines
67 KiB
Python
import sys, yaml
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from easydict import EasyDict as edict
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from concurrent.futures import ThreadPoolExecutor
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sys.path.extend(['..','../AIlib2' ])
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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
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import cv2,os,time
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from segutils.segmodel import SegModel
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from yolov5 import yolov5Model
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from stdc import stdcModel
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from segutils.trafficUtils import tracfficAccidentMixFunction
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from models.experimental import attempt_load
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from utils.torch_utils import select_device
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from utilsK.queRiver import get_labelnames,get_label_arrays,save_problem_images,riverDetSegMixProcess,draw_painting_joint
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from ocrUtils.ocrUtils import CTCLabelConverter,AlignCollate
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from trackUtils.sort import Sort,track_draw_boxAndTrace,track_draw_trace_boxes,moving_average_wang,drawBoxTraceSimplied
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from trackUtils.sort_obb import OBB_Sort,obbTohbb,track_draw_all_boxes,track_draw_trace
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from obbUtils.shipUtils import OBB_infer,OBB_tracker,draw_obb,OBB_tracker_batch
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from utilsK.noParkingUtils import mixNoParking_road_postprocess
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from obbUtils.load_obb_model import load_model_decoder_OBB
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import numpy as np
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import torch,glob
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import tensorrt as trt
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from utilsK.masterUtils import get_needed_objectsIndex
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from copy import deepcopy
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from scipy import interpolate
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from utilsK.drownUtils import mixDrowing_water_postprocess
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#import warnings
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#warnings.filterwarnings("error")
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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]]
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def drawAllBox(preds,imgDraw,label_arraylist,rainbows,font):
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for box in preds:
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#cls,conf,xyxy = box[0],box[5], box[1:5]
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#print('#'*20,'line47',box)
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cls,conf,xyxy = box[5],box[4], box[0:4] ##2023.08.03,修改了格式
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#print('#####line46 demo.py:', cls,conf,xyxy, len(label_arraylist),len(rainbows) )
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imgDraw = draw_painting_joint(xyxy,imgDraw,label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font,socre_location="leftBottom")
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return imgDraw
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def get_drawPar(par):
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labelnames = par['labelnames']
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names=get_labelnames(labelnames)
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mode_paras=par['detModelpara']
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allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
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outfontsize=int(1080/1920*40);###
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label_arraylist = get_label_arrays(names,rainbows,outfontsize=par['txtFontSize'],fontpath="../AIlib2/conf/platech.ttf")
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drawPar={'names':names,'label_arraylist':label_arraylist,'rainbows':rainbows,'font': par['digitFont'],'allowedList':allowedList}
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return drawPar
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def view_bar(num, total,time1,prefix='prefix'):
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rate = num / total
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time_n=time.time()
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rate_num = int(rate * 30)
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rate_nums = np.round(rate * 100)
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r = '\r %s %d / %d [%s%s] %.2f s'%(prefix,num,total, ">" * rate_num, " " * (30 - rate_num), time_n-time1 )
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sys.stdout.write(r)
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sys.stdout.flush()
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'''
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多线程
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'''
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def process_v1(frame):
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#try:
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print('demo.py beging to :',frame[8])
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time00 = time.time()
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H,W,C = frame[0][0].shape
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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])
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time11 = time.time()
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image_array = p_result[1]
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cv2.imwrite(os.path.join('images/results/',frame[8] ) ,image_array)
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bname = frame[8].split('.')[0]
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if len(p_result)==5:
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image_mask = p_result[4]
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cv2.imwrite(os.path.join('images/results/',bname+'_mask.png' ) , (image_mask).astype(np.uint8))
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boxes=p_result[2]
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with open( os.path.join('images/results/',bname+'.txt' ),'w' ) as fp:
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for box in boxes:
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box_str=[str(x) for x in box]
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out_str=','.join(box_str)+'\n'
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fp.write(out_str)
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time22 = time.time()
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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)
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return 'success'
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#except Exception as e:
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# return 'failed:'+str(e)
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def process_video(video,par0,mode='detSeg'):
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cap=cv2.VideoCapture(video)
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if not cap.isOpened():
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print('#####error url:',video)
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return False
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bname=os.path.basename(video).split('.')[0]
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fps = int(cap.get(cv2.CAP_PROP_FPS)+0.5)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH )+0.5)
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)+0.5)
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framecnt=int(cap.get(7)+0.5)
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save_path_AI = os.path.join(par0['outpth'],os.path.basename(video))
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problem_image_dir= os.path.join( par0['outpth'], 'probleImages' )
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os.makedirs(problem_image_dir,exist_ok=True)
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vid_writer_AI = cv2.VideoWriter(save_path_AI, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width,height))
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num=0
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iframe=0;post_results=[];fpsample=30*10
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imgarray_list = []; iframe_list = []
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patch_cnt = par0['trackPar']['patchCnt']
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##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。
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trackPar={'det_cnt':10,'windowsize':29 }
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##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.
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while cap.isOpened():
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ret, imgarray = cap.read() #读取摄像头画面
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iframe +=1
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if not ret:break
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if mode=='detSeg':
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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'])
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elif mode == 'track':
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#sampleCount=10
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imgarray_list.append( imgarray )
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iframe_list.append(iframe )
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if iframe%patch_cnt==0:
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time_patch0 = time.time()
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#retResults,timeInfos = AI_det_track_batch(imgarray_list, iframe_list ,par0['modelPar'],par0['processPar'],par0['sort_tracker'] ,par0['trackPar'],segPar=par0['segPar'])
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retResults,timeInfos = AI_det_track_batch_N(imgarray_list, iframe_list ,par0['modelList'],par0['postProcess'],par0['sort_tracker'],par0['trackPar'])
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print('###line111:',retResults[2])
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###需要保存成一个二维list，每一个list是一帧检测结果。
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###track_det_result 内容格式：x1, y1, x2, y2, conf, cls,iframe,trackId
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time_patch2 = time.time()
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frame_min = iframe_list[0];frame_max=iframe_list[-1]
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for iiframe in range(frame_min,frame_max+1):
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img_draw = imgarray_list[ iiframe- frame_min ]
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img_draw = drawBoxTraceSimplied(retResults[1] ,iiframe, img_draw,rainbows=par0['drawPar']['rainbows'],boxFlag=True,traceFlag=True,names=par0['drawPar']['names'] )
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ret = vid_writer_AI.write(img_draw)
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view_bar(iiframe, framecnt,time.time(),prefix=os.path.basename(video))
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imgarray_list=[];iframe_list=[]
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elif mode =='obbTrack':
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imgarray_list.append( imgarray )
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iframe_list.append(iframe )
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if iframe%patch_cnt==0:
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time_patch0 = time.time()
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track_det_results, timeInfos = OBB_tracker_batch(imgarray_list,iframe_list,par0['modelPar'],par0['obbModelPar'],par0['sort_tracker'],par0['trackPar'],segPar=None)
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print( timeInfos )
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#对结果画图
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track_det_np = track_det_results[1]
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frame_min = iframe_list[0];frame_max=iframe_list[-1]
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for iiframe in range(frame_min,frame_max+1):
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img_draw = imgarray_list[ iiframe- frame_min ]
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if len( track_det_results[2][ iiframe- frame_min]) > 0:
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img_draw = draw_obb( track_det_results[2][iiframe- frame_min ] ,img_draw,par0['drawPar'])
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if True:
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frameIdex=12;trackIdex=13;
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boxes_oneFrame = track_det_np[ track_det_np[:,frameIdex]==iiframe ]
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###在某一帧上，画上轨迹
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track_ids = boxes_oneFrame[:,trackIdex].tolist()
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boxes_before_oneFrame = track_det_np[ track_det_np[:,frameIdex]<=iiframe ]
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for trackId in track_ids:
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boxes_before_oneFrame_oneId = boxes_before_oneFrame[boxes_before_oneFrame[:,trackIdex]==trackId]
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xcs = boxes_before_oneFrame_oneId[:,8]
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ycs = boxes_before_oneFrame_oneId[:,9]
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[cv2.line(img_draw, ( int(xcs[i]) , int(ycs[i]) ),
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( int(xcs[i+1]),int(ycs[i+1]) ),(255,0,0), thickness=2)
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for i,_ in enumerate(xcs) if i < len(xcs)-1 ]
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ret = vid_writer_AI.write(img_draw)
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#sys.exit(0)
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#print('vide writer ret:',ret)
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imgarray_list=[];iframe_list=[]
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view_bar(iframe, framecnt,time.time(),prefix=os.path.basename(video))
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else:
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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'])
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if mode not in [ 'track','obbTrack']:
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image_array = p_result[1];num+=1
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ret = vid_writer_AI.write(image_array)
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view_bar(num, framecnt,time.time(),prefix=os.path.basename(video))
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##每隔 fpsample帧处理一次，如果有问题就保存图片
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if (iframe % fpsample == 0) and (len(post_results)>0) :
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parImage=save_problem_images(post_results,iframe,par0['names'],streamName=bname,outImaDir=problem_image_dir,imageTxtFile=False)
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post_results=[]
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if len(p_result[2] )>0:
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post_results.append(p_result)
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vid_writer_AI.release();
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def det_track_demo_N(business ):
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####河道巡检的跟踪模型参数
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if opt['business'] == 'river' or opt['business'] == 'river2' or opt['business'] == 'riverT':
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from utilsK.queRiver import riverDetSegMixProcess_N
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par={
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'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
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'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%(opt['business']), ###检测类别对照表
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'max_workers':1, ###并行线程数
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'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':10,'windowsize':29,'patchCnt':100},
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'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
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'models':
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[
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{
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'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
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#'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business'] ),
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'name':'yolov5',
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'model':yolov5Model,
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'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 } },
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},
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{
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'weight':"../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
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#'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business'] ),
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'par':{
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'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
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'model':stdcModel,
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'name':'stdc'
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}
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],
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'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
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'postFile': '../AIlib2/weights/conf/%s/para.json'%( opt['business'] ),###后处理参数文件
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'txtFontSize':80,###文本字符的大小
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'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置
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#'testImgPath':'images/videos/river',###测试图像的位置
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'testImgPath':'images/%s'%(opt['business']),###测试图像的位置
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'testOutPath':'images/results/',###输出测试图像位置
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}
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if opt['business'] == 'highWay2':
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from segutils.trafficUtils import tracfficAccidentMixFunction_N
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par={
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'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
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'labelnames':"../AIlib2/weights/conf/%s/labelnames.json"%( opt['business'] ), ###检测类别对照表
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'max_workers':1, ###并行线程数
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'trackPar':{'sort_max_age':2,'sort_min_hits':3,'sort_iou_thresh':0.2,'det_cnt':5,'windowsize':29,'patchCnt':100},
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'postProcess':{'function':tracfficAccidentMixFunction_N,
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'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 }
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},
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'models':
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[
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{
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'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
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#'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business'] ),
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'name':'yolov5',
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'model':yolov5Model,
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'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 } },
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},
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{
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#'weight':"../weights/%s/AIlib2/%s/dmpr_%s.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
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'weight':'../AIlib2/weights/conf/%s/stdc_360X640.pth'%(opt['business'] ),
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'par':{
|
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'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
|
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'model':stdcModel,
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'name':'stdc'
|
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}
|
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],
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'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
|
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'postFile': '../AIlib2/weights/conf/%s/para.json'%(opt['business'] ),###后处理参数文件
|
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'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
|
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#'testImgPath':'images/trafficAccident/8.png',###测试图像的位置
|
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#'testImgPath':'/home/chenyukun/777-7-42.mp4',###测试图像的位置
|
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'testImgPath':'/mnt/thsw2/DSP2/weights/highWay2/images/',
|
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'testOutPath':'images/results/',###输出测试图像位置
|
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}
|
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par['postProcess']['pars']['modelSize'] = par['models'][1]['par']['modelSize']
|
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|
||
if opt['business'] == 'noParking':
|
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from utilsK.noParkingUtils import mixNoParking_road_postprocess_N
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par={
|
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'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},
|
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'postProcess':{'function':mixNoParking_road_postprocess_N,
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'pars': { 'roundness': 0.3, 'cls': 9, 'laneArea': 10, 'laneAngleCha': 5 ,'RoadArea': 16000,'fitOrder':2}
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} ,
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'models':
|
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[
|
||
{
|
||
'weight':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径
|
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#'weight':'../AIlib2/weights/conf/%s/yolov5.pt'%(opt['business'] ),
|
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'name':'yolov5',
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'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 )
|
||
|
||
|
||
|