update cityMangement3 weights

demo.py

@@ -179,6 +179,27 @@ def detSeg_demo(opt):
             'testImgPath':'images/river2/', 
             'testOutPath':'images/results/',###输出测试图像位置
         }
+    if opt['business'] == 'riverT':
+        par={
+            'device':'0', ###显卡号,如果用TRT模型，只支持0（单显卡）
+            'labelnames':"../AIlib2/weights/conf/riverT/labelnames.json", ###检测类别对照表
+            'max_workers':1, ###并行线程数    
+            'Detweights':"../weights/%s/AIlib2/%s/yolov5_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###检测模型路径
+            'detModelpara':[{"id":str(x),"config":{"k1":"v1","k2":"v2"}} for x in [0,1,2,3,4,5,6,7,8,9]  ],###控制哪些检测类别显示，输出
+            'seg_nclass':2,###分割模型类别数目，默认2类
+            'segRegionCnt':1,###分割模型结果需要保留的等值线数目       
+            'segPar':{'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True,#分割模型预处理参数
+                        'mixFunction':{'function':riverDetSegMixProcess,'pars':{'slopeIndex':[1,3,4,7], 'riverIou':0.1}} #分割和检测混合处理的函数 
+                        },
+            'Segweights' : "../weights/%s/AIlib2/%s/stdc_360X640_%s_fp16.engine"%(opt['gpu'], opt['business'] ,opt['gpu']  ),###分割模型权重位置
+            'postFile': '../AIlib2/weights/conf/riverT/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),'segLineShow':True,'waterLineWidth':3},###显示框、线、数字设置
+            'testImgPath':'images/riverT/', 
+            'testOutPath':'images/results/',###输出测试图像位置
+        }
+
+    
 
     if opt['business'] == 'highWay2':
         par={
@@ -496,56 +517,7 @@ def detSeg_demo2(opt):
                 'testImgPath':'/mnt/thsw2/DSP2/weights/cityMangement2_1102/images/debug',
                 'testOutPath':'images/results/',###输出测试图像位置    
             }
-            
-    if opt['business'] == 'crackMeasurement':
-        from AI import AI_process_C
-        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':{
-                    'function':Crack_measure,
-                    'pars':{'dsx':(123-30)*1000/35*0.004387636  }
-                    }, 
-                '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')
@@ -561,12 +533,8 @@ def detSeg_demo2(opt):
     #开始测试
     for imgUrl in   imgpaths:
         img = cv2.imread(imgUrl);bname = os.path.basename(imgUrl)
-        if opt['business'] == 'crackMeasurement':
-            ret,timeInfos = AI_process_C([img],modelList,par['postProcess'])
-            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] ) )
-        else:
-            ret,timeInfos = AI_process_N([img],modelList,par['postProcess'])
+        
+        ret,timeInfos = AI_process_N([img],modelList,par['postProcess'])
         timeInfos=bname+':'+timeInfos
         print(timeInfos )
         if len(ret)>0:
@@ -1152,8 +1120,8 @@ def customization_demo(opt):
                 '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':"../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',
@@ -1163,11 +1131,13 @@ def customization_demo(opt):
                         },
                       
                         {                      
-                            'weight' : '/mnt/thsw2/DSP2/weights/ocr2/crnn_ch_2080Ti_fp16_192X32.engine',    
+                            #'weight' : '../weights/%s/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(opt['gpu'], opt['gpu']),    
+                            'weight':'../AIlib2/weights/conf/ocr2/crnn_ch.pth',
                             'name':'ocr',
                             'model':ocrModel,
                             'par':{
-                                'char_file':'/home/thsw2/WJ/src/OCR/benchmarking-chinese-text-recognition/src/models/CRNN/data/benchmark.txt',
+                                'char_file':'../AIlib2/weights/conf/ocr2/benchmark.txt',
+                                'weight' : '../weights/%s/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(opt['gpu'], opt['gpu']), 
                                 'mode':'ch',
                                 'nc':3,
                                 'imgH':32,
@@ -1203,8 +1173,8 @@ def customization_demo(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':0.1,'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 } }
@@ -1274,18 +1244,17 @@ def customization_demo(opt):
 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']
+    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']
-    
-    # forest 、 ocr2  、ocr_en 、 river 、 road 、 ship ,目前都没有在用
+    businessAll = ['riverT']
     
+    # forest 、 ocr2  、ocr_en 、 river 、 road 、 ship ,目前都没有在用 
     for busi in businessAll:
         print('-'*40,'beg to test ',busi,'-'*40)
         opt={'gpu':'2080Ti','business':busi}
-        if opt['business'] in ['highWay2','river2','drowning','noParking','river',"illParking","cityMangement2"]:
+        if opt['business'] in ['highWay2','river2','drowning','noParking','river',"illParking","cityMangement2","riverT"]:
             detSeg_demo(opt) 
-        elif  opt['business'] in ['cityMangement3','crackMeasurement'] : 
+        elif  opt['business'] in ['cityMangement3'] : 
             detSeg_demo2(opt)
         elif  opt['business'] in ['crowdCounting'] :
             crowd_demo(opt)

demo3.0.py

@@ -198,7 +198,7 @@ def process_video(video,par0,mode='detSeg'):
    
 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' : 
+    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）
@@ -216,8 +216,8 @@ def det_track_demo_N(business ):
                         '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']  ),
+                        '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,
@@ -230,7 +230,7 @@ def det_track_demo_N(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':'images/videos/river',###测试图像的位置
-            'testImgPath':'images/river2',###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
             
         }
@@ -871,11 +871,11 @@ def customization_demo(opt):
                         },
                       
                         {                      
-                            'weight' : '/mnt/thsw2/DSP2/weights/ocr2/crnn_ch_2080Ti_fp16_192X32.engine',    
+                            'weight' : '../weights/%s/AIlib2/ocr2/crnn_ch_%s_fp16_192X32.engine'%(opt['gpu'], opt['gpu']),  
                             'name':'ocr',
                             'model':ocrModel,
                             'par':{
-                                'char_file':'/home/thsw2/WJ/src/OCR/benchmarking-chinese-text-recognition/src/models/CRNN/data/benchmark.txt',
+                                'char_file':'../AIlib2/weights/conf/ocr2/chars.txt',
                                 'mode':'ch',
                                 'nc':3,
                                 'imgH':32,
@@ -1120,8 +1120,8 @@ def crowd_demo(opt):
 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']
-    businessAll = [ 'channel2'     ]
+    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'     ]
     
     for busi in businessAll:
         print('-'*40,'beg to test:',busi,'-'*40)