wangjin0928
2 months ago
2 changed files with 92 additions and 35 deletions
+ 89
- 32
demo.py
View File
| @@ -485,8 +485,8 @@ def detSeg_demo2(opt): | |||
| }, | |||
| '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':"../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 } } | |||
| @@ -521,11 +521,38 @@ def detSeg_demo2(opt): | |||
| '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', | |||
| '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 | |||
| } | |||
| if opt['business'] == 'forest2': | |||
| from utilsK.crowdGather import gather_post_process | |||
| 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 | |||
| 'postProcess':{'function':gather_post_process,'pars':{'pedestrianId':2,'crowdThreshold':4,'gatherId':6,'distancePersonScale':2.0}}, | |||
| 'models': | |||
| [ | |||
| { | |||
| #'weight':"../weights/%s/AIlib2/forest2/yolov5_%s_fp16.engine"%(opt['gpu'], opt['gpu'] ),###检测模型路径 | |||
| 'weight':'/mnt/thsw2/DSP2/weights/forest2/yolov5.pt',###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':5,###文本字符的大小 | |||
| 'digitFont': { 'line_thickness':1,'boxLine_thickness':1, 'fontSize':0.5,'waterLineColor':(0,255,255),'waterLineWidth':1},###显示框、线设置 | |||
| #'testImgPath':'../AIdemo2/images/forest2/',###测试图像的位置 | |||
| 'testImgPath':'/mnt/thsw2/DSP2/weights/forest2/images_visdrone', | |||
| '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') | |||
| @@ -573,32 +600,34 @@ def det_demo(business ): | |||
| '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/forest/',###测试图像的位置 | |||
| #'testImgPath':'/mnt/thsw2/DSP2/weights/forest2/', | |||
| '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 | |||
| 'gpuname':opt['gpu'],###显卡名称 | |||
| 'max_workers':1, ###并行线程数 | |||
| 'trtFlag_det':True,###检测模型是否采用TRT | |||
| 'trtFlag_seg':False,###分割模型是否采用TRT | |||
| 'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径 | |||
| 'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出 | |||
| #'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [] ], | |||
| # if opt['business'] == 'forest2': | |||
| # par={ | |||
| # 'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡） | |||
| # 'labelnames':"../AIlib2/weights/conf/forest2/labelnames.json", ###检测类别对照表 | |||
| # 'gpuname':opt['gpu'],###显卡名称 | |||
| # 'max_workers':1, ###并行线程数 | |||
| # 'trtFlag_det':True,###检测模型是否采用TRT | |||
| # 'trtFlag_seg':False,###分割模型是否采用TRT | |||
| # 'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu'] ),###检测模型路径 | |||
| # 'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6] ],###控制哪些检测类别显示、输出 | |||
| # #'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [] ], | |||
| 'slopeIndex':[],###岸坡类别（或者其它业务里的类别），不与河道（分割的前景区域）计算交并比，即不论是否在河道内都显示。 | |||
| 'seg_nclass':2,###分割模型类别数目，默认2类 | |||
| 'segRegionCnt':0,###分割模型结果需要保留的等值线数目 | |||
| 'segPar':None,###分割模型预处理参数 | |||
| 'Segweights' : None,###分割模型权重位置 | |||
| 'postFile': '../AIlib2/weights/conf/forest/para.json',###后处理参数文件 | |||
| 'txtFontSize':80,###文本字符的大小 | |||
| 'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置 | |||
| 'testImgPath':'../AIdemo2/images/forest2/',###测试图像的位置 | |||
| 'testOutPath':'images/results/',###输出测试图像位置 | |||
| } | |||
| # 'slopeIndex':[],###岸坡类别（或者其它业务里的类别），不与河道（分割的前景区域）计算交并比，即不论是否在河道内都显示。 | |||
| # 'seg_nclass':2,###分割模型类别数目，默认2类 | |||
| # 'segRegionCnt':0,###分割模型结果需要保留的等值线数目 | |||
| # 'segPar':None,###分割模型预处理参数 | |||
| # 'Segweights' : None,###分割模型权重位置 | |||
| # 'postFile': '../AIlib2/weights/conf/forest/para.json',###后处理参数文件 | |||
| # 'txtFontSize':80,###文本字符的大小 | |||
| # 'digitFont': { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3},###显示框、线设置 | |||
| # #'testImgPath':'../AIdemo2/images/forest2/',###测试图像的位置 | |||
| # 'testImgPath':'/mnt/thsw2/DSP2/weights/forest2/images_cloud_FP', | |||
| # 'testOutPath':'images/results/',###输出测试图像位置 | |||
| # } | |||
| ###车辆巡检参数 | |||
| @@ -1129,9 +1158,9 @@ def customization_demo(opt): | |||
| 'postProcess':{'function':channel2_post_process,'name':'channel2','pars':{'objs':[2],'wRation':1/6.0,'hRation':1/6.0,'smallId':0,'bigId':3,'newId':4,'recScale':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':"../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.pt', | |||
| #'weight':'/mnt/thsw2/DSP2/weights/channel2/yolov5_2080Ti_fp16.engine', | |||
| 'name':'yolov5', | |||
| 'model':yolov5Model, | |||
| @@ -1165,6 +1194,7 @@ def customization_demo(opt): | |||
| '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/weights/channel2/video_for_sales/', | |||
| #'testImgPath':'/home/thsw2/WJ/src/OCR/shipNames', | |||
| #'testImgPath':'images/tt/', | |||
| 'testOutPath':'images/results/',###输出测试图像位置 | |||
| } | |||
| @@ -1264,6 +1294,7 @@ def customization_demo(opt): | |||
| #测试视频 | |||
| ###如果是视频文件 | |||
| ''' | |||
| for video in videopaths: | |||
| cap=cv2.VideoCapture(video) | |||
| fps,width,height,framecnt = get_video_para(cap) | |||
| @@ -1296,13 +1327,39 @@ def customization_demo(opt): | |||
| ret = vid_writer_AI.write(img0) | |||
| view_bar(iframe, framecnt,time.time(),prefix=os.path.basename(video)) | |||
| vid_writer_AI.release(); | |||
| ''' | |||
| def AI_process_C_multi( ps ): | |||
| return AI_process_C( ps[0] ,ps[1],ps[2] ) | |||
| ###用视频文件做一个多线程测试 | |||
| max_workers=4 | |||
| bs=4 | |||
| for video in videopaths: | |||
| cap=cv2.VideoCapture(video) | |||
| fps,width,height,framecnt = get_video_para(cap) | |||
| print('-'*10,' line1307 fps:{}, width:{},height:{},framecnt:{} '.format( fps,width,height,framecnt) ) | |||
| iframe =0 | |||
| parsIns=[] | |||
| while cap.isOpened(): | |||
| ret, img = cap.read() #读取摄像头画面 | |||
| if not ret:break | |||
| parsIns.append( [ [img],modelList,par['postProcess'] ] ) | |||
| iframe+=1 | |||
| if iframe%bs==0: | |||
| with ThreadPoolExecutor(max_workers=max_workers) as t: | |||
| results = t.map(AI_process_C_multi, parsIns) | |||
| results = list(results) | |||
| print(iframe,len( parsIns )) | |||
| parsIns=[] | |||
| view_bar(iframe, framecnt,time.time(),prefix=os.path.basename(video)) | |||
| if __name__=="__main__": | |||
| #jkm_demo() | |||
| businessAll=['river2','AnglerSwimmer', 'countryRoad','forest2', 'pedestrian' , 'smogfire' , 'vehicle','ship2',"highWay2","channelEmergency","cityMangement","drowning","noParking","illParking",'cityMangement2',"cityRoad","crowdCounting",'cityMangement3','ocr_en','ocr_ch','pothole','crackMeasurement','channel2','riverT'] | |||
| businessAll=['river2','AnglerSwimmer', 'countryRoad','forest2', 'pedestrian' , 'smogfire' , 'vehicle','ship2',"highWay2","channelEmergency","cityMangement","drowning","noParking","illParking",'cityMangement2',"cityRoad","crowdCounting",'cityMangement3','ocr_en','ocr_ch','pothole','crackMeasurement','channel2','riverT'] | |||
| businessAll = ['channel2'] | |||
| businessAll = ['forest2'] | |||
| # forest 、 ocr2 、ocr_en 、 river 、 road 、 ship ,目前都没有在用 | |||
| for busi in businessAll: | |||
| @@ -1310,7 +1367,7 @@ if __name__=="__main__": | |||
| opt={'gpu':'2080Ti','business':busi} | |||
| if opt['business'] in ['highWay2','river2','drowning','noParking','river',"illParking","cityMangement2","riverT"]: | |||
| detSeg_demo(opt) | |||
| elif opt['business'] in ['cityMangement3'] : | |||
| elif opt['business'] in ['cityMangement3','forest2'] : | |||
| detSeg_demo2(opt) | |||
| elif opt['business'] in ['crowdCounting'] : | |||
| crowd_demo(opt) | |||
+ 3
- 3
demo3.0.py
View File
| @@ -443,8 +443,8 @@ def det_track_demo_N(business ): | |||
| '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/', | |||
| 'testImgPath':'images/%s/'%(opt['business']), | |||
| #'testImgPath':'/mnt/thsw2/DSP2/weights/cityMangement3/images/', | |||
| 'testOutPath':'images/results/',###输出测试图像位置 | |||
| } | |||
| @@ -1121,7 +1121,7 @@ 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 = [ 'riverT' ] | |||
| businessAll = [ 'forest2' ] | |||
| for busi in businessAll: | |||
| print('-'*40,'beg to test:',busi,'-'*40) | |||
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